Professor Ali Emrouznejad

Inverse data envelopment analysis (DEA) models are supplementary techniques for performance evaluation and further performance analysis. Cost and revenue efficiency give decision makers valuable information, yet in many real-world issues, we only have cost information rather than full price information. The literature on the inverse DEA suffers from a theoretical gap in dealing with the cost and revenue efficiency when there is no information about the prices. In this research, new inverse DEA models based on revenue and cost efficiency without pricing information are developed. The proposed models are applied to a real-world problem and they are used for the performance assessment and cost analysis of 154 public hospitals.

This book contains the proceedings of the 10th International Conference on Internet of Things, Big Data and Security. This year, IoTBDS is held in Porto, Portugal, on April 6-8, 2025. It was sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). The Internet of Things (IoT) is a platform that allows a network of devices (sensors, smart meters, etc.) to communicate, analyse data, and process information collaboratively in the service of individuals or organisations. The IoT network can generate large amounts of data in a variety of formats and use different protocols that can be stored and processed in the cloud. The conference looks to address the issues surrounding IoT devices, their interconnectedness and the services they may offer, including efficient, effective and secure analysis of IoT-generated data using machine learning and other advanced techniques, models and tools, and issues of security, privacy and trust that will emerge as IoT technologies mature and become part of our everyday lives.Big Data (BD) has core values of volume, velocity, variety and veracity. After collecting much data from IoT, BD can be jointly used with machine learning, AI, statistical and other advanced techniques, models and methods, which can create values for people and organisations adopting it, since forecasting, deep analysis and analytics can help identify weaknesses and make improvements based on different analysis. Maintaining a high level of security and privacy for data in IoT is crucial, and we welcome recommendations, solutions, demonstrations, and best practices for all forms of security and privacy for IoT and BD. IoTBDS 2025 received 73 paper submissions from 27 countries, of which 21.92% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least 2, but usually 3 or more, members of the International Program Committee, which is composed of established researchers and domain experts. The high quality of the IoTBDS 2025 program is enhanced by the keynote lectures delivered by distinguished speakers who are renowned experts in their fields: Ana Aguiar (Faculty of Engineering University of Porto/ Instituto de Telecomunicações, Portugal), Schahram Dustdar (Vienna University of Technology, Austria) and Loic Bachelart (Microsoft, France). The conference is complemented by a Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives, chaired by Fiammetta Marulli and Lelio Campanile. All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index. Several awards, based on the combined marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as assessed by session chairs at the conference venue, are conferred at the conference's closing session as recognition for the best contributions. The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are reported here. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organising a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly competent team. We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of IoTBDS, details of which are available at https://iotbds.scitevents.org

Although there is a growing number of research articles investigating the performance in the banking industry, research on Chinese banking efficiency is rather focused on discussing rankings to the detriment of unveiling its productive structure in light of banking competition. This issue is of utmost importance considering the relevant transformations in the Chinese economy over the last decades. This is a development of a two-stage network production process (production and intermediation approaches in banking, respectively) to evaluate the efficiency level of Chinese commercial banks. In the second stage regression analysis, an integrated Multi-Layer Perceptron/Hidden Markov model is used for the first time to unveil endogeneity among banking competition, contextual variables, and efficiency levels of the production and intermediation approaches in banking. The competitive condition in the Chinese banking industry is measured by Panar–Rosse H-statistic and Lerner index under the Ordinary Least Square regression. Findings reveal that productive efficiency appears to be positively impacted by competition and market power. Second, credit risk analysis in older local banks, which focus the province level, would possibly be the fact that jeopardizes the productive efficiency levels of the entire banking industry in China. Thirdly, it is found that a perfect banking competition structure at the province level and a reduced market power of local banks are drivers of a sound banking system. Finally, our findings suggest that concentration of credit in a few banks leads to an increase in bank productivity.

This book provides insight into the use of evolutionary computations in real-world applications. This edited book allows the reader to analyze the point of view of each contributor regarding how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. The spectrum of real-world optimization problems dealt with in this book includes, among others, application of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. Throughout the book, the reader will find not only problems with different degrees of complexity, but also with different practical requirements, user constraints, and a variety of MOEC solution approaches. This book is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.

•A synthesized hands-on literature review of 50 years of Data Envelopment Analysis.•A structured, six-stage framework to streamline DEA analyses.•A practical guide to implement non-parametric efficiency analysis. Data Envelopment Analysis (DEA) has emerged as a powerful analytical tool, revolutionising the field of Operational Research (OR) and contributing to advancements in performance evaluation methodologies. This practical literature review delves into the extensive body of research surrounding DEA, focusing particularly on its evolution within the last 50 years. Drawing upon a comprehensive analysis of publications in top-tier OR journals this literature review paper offers analysis of DEA's development, highlighting key milestones, methodological advancements, and emerging trends. Central to this exploration is the introduction of the COOPER-framework—a structured approach derived from influential papers in the field—that provides a state-of-the-art synthesis of DEA methodologies. Emphasizing non-parametric models and addressing the challenges posed by complex decision-making environments, the COOPER-framework serves as a valuable resource for both experienced scholars and newcomers to the field. By incorporating feedback loops to navigate interconnected decisions, the framework ensures the reliability and robustness of DEA analyses. Through this literature review, we aim to not only refine existing methodologies but also provide a practical tool for researchers, fostering collaboration and driving further innovation in the field of DEA.

Power systems are increasingly collecting large amounts of data due to the expansion of the Internet of Things into power grids. In a smart grids scenario, a huge number of intelligent devices will be connected with almost no human intervention characterizing a machine-to-machine scenario, which is one of the pillars of the Internet of Things. The book characterizes and evaluates how the emerging growth of data in communications networks applied to smart grids will impact the grid efficiency and reliability. Additionally, this book discusses the various security concerns that become manifest with Big Data and expanded communications in power grids.Provide a general description and definition of big data, which has been gaining significant attention in the research community.Introduces a comprehensive overview of big data optimization methods in power system.Reviews the communication devices used in critical infrastructure, especially power systems; security methods available to vet the identity of devices; and general security threats in CI networks.Presents applications in power systems, such as power flow and protection.Reviews electricity theft concerns and the wide variety of data-driven techniques and applications developed for electricity theft detection.

This paper provides a comprehensive review of slacks-based measure (SBM) models within the nonparametric data envelopment analysis (DEA) framework. The review reveals that the development and modifications of SBM models have progressed in multiple directions. Key areas of modification include (1) the nature of inputs and outputs, (2) data specificity, (3) super-efficiency for ranking decision-making units, (4) the inclusion of networks in organizational structures or production processes, (5) the dynamic nature of the analysis, and (6) various other methodological aspects. Increasingly, complex SBM models addressing multiple aspects, such as input/output characteristics and data specificity, are appearing in the literature. Another notable trend is the integration of various methodological proposals from different authors into unified SBM models. Some publications even attempt to forecast future efficiency levels and incorporate large datasets (big data). Despite numerous modifications and advancements, SBM models still lack robust statistical inference capabilities, unlike radial models, which possess more developed statistical foundations.

This paper provides a comprehensive analysis of Professor Rajiv Banker's significant impact on the field of Data Envelopment Analysis (DEA). Through an extensive review of his scholarly contributions, we explore three major clusters within DEA research: (1) Returns-to-Scale (RTS) and Most Productive Scale Size (MPSS), (2) Statistical Inference in DEA, and (3) Contextual Analysis. Banker's pioneering research has significantly advanced DEA methodologies, addressing fundamental challenges related to scale efficiency, statistical robustness, and the influence of contextual variables on performance. His work has bridged theoretical developments and practical applications, influencing diverse fields such as economics, finance, and management science. By examining citation trends and bibliometric data, we trace the evolution and enduring relevance of his contributions, highlighting key papers that have shaped the trajectory of DEA research. This paper also discusses the evolution of DEA models and approaches, including the integration of stochastic elements and second-stage analyses. In recognising Banker's lifetime dedication to DEA, we celebrate his lasting legacy and his transformative influence on both the academic community and practical implementations of DEA worldwide.

The mitigation of climate change is essential for sustainable development, and the transition to renewable energy plays a vital role in this process. Policies aimed at promoting the adoption of renewable energy could enhance sustainable development. This study measures sustainable development by using green total factor productivity (GTFP), which captures the capacity to foster economic growth while minimizing resource consumption and reducing pollution and carbon emissions. Using the slacks-based measure (SBM) and global Malmquist-Luenberger (GML) index, we calculate the GTFP index of 281 Chinese prefectures from 2012 to 2022 based on data from the China City Statistical Yearbook. An increase in GTFP indicates higher output with lower emissions from finite resources, thereby signifying the improvement in sustainable development. By conducting a difference-in-differences (DID) design, we investigate the effects of China's Plan on Clean Energy Accommodation (2018–2020) (PCEA) on sustainable development. Our DID estimates show that the PCEA hinders sustainable development. A series of robustness checks reinforce our main findings. Technological progress is the foundation and driving force of sustainable development. Local governments have to invest in infrastructures to promote renewable energy transition, which may crowd out their expenditure on research and innovation and temporarily impede technological progress. However, the shift toward renewable energy contributes to a cleaner energy structure and reduces carbon intensity. Our heterogeneity analyses reveal that southern cities, resource-based cities, and cities with lower stock of human capital are more vulnerable to energy transition, providing valuable implications for regional sustainable development.

Public bus transit service (PBTS) is recognized as a highly effective mode of transportation, offering accessibility, affordability, and adaptability that contribute to its critical role in transportation networks. The extensive literature on PBTS encompasses various aspects, with mathematical programming emerging as a widely employed methodology to tackle the public bus transit network design and operations planning problem (PBTNDP&OPP). In this paper, first, we employ the critical path method (CPM) to visually map the development of existing literature on the application of mathematical programming in PBTND&OPP by focusing on manuscripts published in top-tier journals. The objective is to identify key subproblems extensively studied in the literature and recently emerging topics. Then, we conduct a comprehensive review of recent applications of mathematical programming in PBTND&OPP, encompassing sustainable and green practices, as well as emerging transportation technologies and modes within PBTS. These two sub-problems have been identified as recently emerged and hot topics in the literature of mathematical programming and PBTND&OPP, based on the provided CPM in the first step. Selected papers for each sub-problem are examined, providing insights into problem formulation, objective functions, decision variables, demand patterns, network structures, and key findings. Based on the literature review, we systematically identify research gaps in each sub-problem and offer directions and suggestions for future studies. While there is a considerable body of literature that has applied mathematical programming to investigate these two emerging topics, our review highlights that the existing literature is still in the early stages of development. Hence, numerous problems relating to these topics remain ripe for exploration through mathematical programming. Examining the effects of sustainable development policies or the introduction of emerging technologies on the reliability and long-term performance of PBTSs represents a significant gap in current research. On the methodological side, the main gap in the literature is the absence of efficient hybrid approaches, where mathematical programming is integrated with other approaches to provide more robust results and to capture the dynamics of PBTSs. Our work aims to advance knowledge in the field of PBTND&OPP and inspire further research endeavors.

Traditionally most cross-selling models in retail banking use demographics information and interactions with marketing as input to statistical models or machine learning algorithms to predict whether a customer is willing to purchase a given financial product or not. We overcome with such limitation by building several models that also use several years of account transaction data. The objective of this study is to analysis credit card transactions of customers, in order to come up with a good prediction in cross-selling products. We use deep-learning algorithm to analyze almost 800,000 credit cards transactions. The results show that such unique data contains valuable information on the customers’ consumption behavior and it can significantly increase the predictive accuracy of a cross-selling model. In summary, we develop an auto-encoder to extract features from the transaction data and use them as input to a classifier. We demonstrate that such features also have predictive power that enhances the performance of the cross-selling model even further.

The 8th International Conference on Computer Science and Application Engineering (CSAE 2024) was successfully held from November 28 to 29, 2024. This annual conference serves as a comprehensive global forum for experts and participants from academia to exchange ideas and present ongoing research in the most state-of-the-art areas of computer science and application engineering. The previous conferences were held in Wuhan (2023)[1], Nanjing (2022)[2], Sanya (2021[3], 2020[4] and 2019[5]), Hohhot (2018)[6]. This year, participants and presenters from six countries and regions, including China, the UK, Singapore, Russia, Japan, and Bosnia and Herzegovina, attended the conference.The proceedings of the CSAE2024 conference feature 26 accepted contributions, selected from 76 submissions through a rigorous peer-review process. These contributions reflect cutting-edge research and innovative applications addressing real-world challenges in computer science and application engineering.This editorial presents a summary of articles and highlight the significance and diverse topics of the articles included in this volume, presenting the latest advancements in research and innovation. The proceedings also feature practical applications addressing pressing real-world issues. Below, we summarize each article's contributions, emphasizing their unique insights and implications for research and practice. [1] https://dl.acm.org/doi/proceedings/10.1145/3627915[2] https://dl.acm.org/doi/proceedings/10.1145/3565387[3] https://dl.acm.org/doi/proceedings/10.1145/3487075[4] https://dl.acm.org/doi/proceedings/10.1145/3424978[5] https://dl.acm.org/doi/proceedings/10.1145/3331453[6] https://dl.acm.org/doi/proceedings/10.1145/3207677

Nonparametric envelopment estimators are often used to estimate the attainable set and its efficient boundary, and to assess efficiency and changes in productivity. Kneip et al. [11] provide asymptotic results that can be used to make inference about expected changes in productivity measured by Malmquist indices and about the sources of productivity changes. These results require convexity of the attainable set, but in a number of situations this assumption is questionable. Recently, Kneip et al. [12] extend these results to allow for possibly non-convex technologies where the DEA estimators are known to be inconsistent. This paper summarizes these results, and explains how researchers should choose the appropriate method in a particular application.

This book provides a comprehensive and practical introduction to Data Envelopment Analysis (DEA). It explains how this non-parametric technique is used to measure performance and extract efficiency from homogeneous entities within a production procedure. It situates DEA within a growing field of productivity analysis and performance measurement, for which numerous models have been proposed. This book encapsulates all of the advances in DEA models proposed in the literature. These models are presented in the context of the GAMS software, which is a powerful tool for mathematical programming models. This book serves two educational purposes: it introduces readers to DEA models and provides examples using GAMS. In addition, the reader is introduced to GAMS programming, as well as innovative and practical applications. GAMS codes are available for free, allowing readers to test and expand the models to meet their specific needs.

•A new directional mix-efficiency measure with uncontrollable inputs & undesirable outputs.•The measure is based on the directional distance function and slacks-based measure.•We measure the environmental efficiency of the OECD countries.•We find that six OECD countries have the capacity to achieve net-zero CO2 emissions.•Findings support the management of OECD countries to determine inefficiency sources. Conventional data envelopment analysis (DEA) models make the assumption of controllable inputs and desirable outputs. However, in many real-world applications, there are two major issues facing the management of decision-making units. The first one is how to deal with uncontrollable inputs whose levels are determined by exogenous fixed factors. The second is how to deal with undesirable outputs that are accompanied by desirable outputs. The effect of the operating environment is frequently captured by uncontrollable inputs and undesirable outputs. The modulation of these two factors into a directional DEA model is still in its infancy in the DEA literature. This paper proposes new directional mix-efficiency measure and slacks-based measure models. These two efficiency models are proposed in the context of uncontrollable inputs and undesirable outputs. The new metric looks at how well the input and/or output mix should change to achieve a fully efficient status by decreasing controllable inputs and undesirable outputs and/or increasing desirable outputs while keeping uncontrollable inputs constant. The new mix-efficiency measure is based on the directional distance function and the slacks-based measure. The usefulness and applicability of the proposed models are assessed by measuring the eco-efficiency of the Organization for Economic Co-Operation and Development (OECD) countries. The aim of the application is to measure efficiency in the context of NetZero, with a specific focus on reducing CO2 emissions. The findings reveal that six countries—France, Luxembourg, Germany, Norway, Sweden, and the UK—have achieved eco-efficiency; therefore, these countries function in the constant returns-to-scale (CRS) region.

This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi -Criteria Decision -Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added.

Evaluating the efficiency of universities is essential to gain sustained competitive advantage for regional/national development, especially from the long-run perspective. In this paper, we develop an improved game cross-efficiency (GCE) approach to assess Chinese " 985 Project " universities considering dual-role factors. First, building on the concept of " joint technology " and the GCE approach, we provide a new approach for efficiency analysis with dual-role factors. In addition, we identify a potential inconsistency problem while searching the Nash equilibrium point from a computational perspective. Importantly, we propose an improved searching process that is consistent with the original GCE paradigm. Finally, we illustrate the proposed approach by applying it to " 985 Project " universities in China. The findings validate the importance of adopting the proposed improved approach. ARTICLE HISTORY

Data Envelopment Analysis (DEA) has emerged as a powerful tool in the realm of performance assessment, efficiency measurement, and decision-making across various sectors. These proceedings include selected papers presented at DEA45, a conference organized by the Centre for Business Analytics in Practice at the University of Surrey in September 2024. DEA45 was convened not only as a platform for showcasing the latest developments in DEA, but also to commemorate the 45th Anniversary of the seminal paper by Charnes, Cooper, and Rhodes,1 which introduced DEA for the first time in 1978. As we reflect on the remarkable journey of DEA over the past four and a half decades, DEA45 provided an invaluable opportunity for scholars, practitioners, and enthusiasts to celebrate the enduring impact of Charnes, Cooper, and Rhodes’ pioneering work and to chart the future trajectory of DEA research and applications. In this comprehensive volume, we bring together a collection of 24 papers that delve into diverse aspects of DEA, showcasing its versatility and applicability in addressing real-world challenges. The chapters in this book span a wide spectrum of topics, ranging from methodological advancements to practical applications in different industries and contexts. Each chapter offers distinct perspectives, methodologies, and empirical discoveries, enriching the diverse landscape of DEA literature. This volume begins with a tribute to Prof. Jon A. Chilingerian, a pioneer in the field whose contributions have significantly shaped the landscape of DEA research. From foundational concepts to cutting-edge methodologies, these proceedings cover a breadth of topics, including inference in dynamic, nonparametric models of production, Bayesian approaches for bias correction, and applications in various sectors such as theatrical firms, ports, and thermal energy generation plants. Exploring the intersection of DEA with other analytical techniques, such as fuzzy logic, path-based models, and intuitionistic fuzzy models, we uncover new avenues for enhancing decision-making processes and gaining robust insights. Moreover, we examine the impact of DEA in diverse domains, including banking, education, healthcare, and environmental sustainability. In the face of the unprecedented challenges posed by events such as the COVID-19 pandemic, DEA provides valuable insights into evaluating and enhancing resource allocation and performance within regional health systems. Furthermore, this volume sheds light on the global economic landscape, offering comprehensive analyses of economic performance over time and space through the lens of DEA and the Malmquist index. As we navigate through the intricacies of efficiency measurement and performance evaluation, we also explore the practical implications of DEA in policy formulation and decision support. From identifying best practices in educational funding to informing policy decisions in public basic education and electric power transmission, DEA serves as an invaluable tool for informed decision-making and policy design. Lastly, we introduce DEA-Viz, a software tool designed to facilitate the visualization of DEA problems, enhancing the accessibility and usability of this powerful analytical framework. In assembling this collection of papers, we aim to provide researchers, practitioners, and policymakers with a comprehensive resource that not only showcases the state-of-the-art in DEA research but also inspires future advancements and applications. We hope that this volume serves as a catalyst for further exploration and innovation in the ever-evolving field of Data Envelopment Analysis. We extend our heartfelt gratitude to all the authors whose dedication, expertise, and scholarly contributions have made this volume possible. Additionally, we extend our sincere thanks to all the reviewers whose invaluable feedback and constructive critiques have enhanced the quality and rigor of each chapter. We would also like to express special thanks to Springer and Springer Production, especially Jialin Yan and Christian Rauscher, who supported the publication of this volume in the Springer series of Lecture Notes in Operational Research. Finally, it is essential to clarify that the responsibility for the content, accuracy, and interpretations presented in each chapter lies solely with the respective authors. As editors, we facilitate the compilation and organization of the material, but we do not assume any responsibility for errors, inaccuracies, or omissions that may be present within individual chapters. While every effort has been made to ensure the quality and integrity of the content, readers should exercise their judgment and discretion when interpreting and utilizing the information provided.

This book presents selected proceedings of the International Conference on Business Analytics in Practice (ICBAP2024), which was held on January 8–11, 2024, at the University of Sharjah, UAE.The book presents advanced modeling and examples to explore the practical applications of business analytics across various industries and domains. In addition, it dives deep into the world of data-driven decision-making, showcasing real-world case studies and best practices to illustrate how organizations can harness the power of analytics to optimize their decision-making processes. From descriptive analytics to predictive modeling and prescriptive analytics, readers will gain valuable insights into the different techniques and methodologies employed in business analytics.

This book contains the proceedings of the 21st International Conference on Smart Business Technologies (ICSBT 2024). This year, ICSBT is held in collaboration with the ESEO, which hosts this event in Dijon, France, on July 9-11, 2024. It was sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). ICSBT 2024 was also organized in cooperation with the ACM Special Interest Group on Management Information Systems. The International Conference on Smart Business Technologies (formerly known as ICE-B - International Conference on e-Business), aims at bringing together researchers and practitioners who work on e-Business technology and its applications. The scope of the conference covers low-level technological issues, such as technology platforms, internet of things, artificial intelligence, data science and web services, but also higher-level issues, such as business processes, business intelligence, digital twins, value setting and business strategy. Furthermore, it covers different research approaches (like qualitative cases, experiments, forecasts, and simulations) to address these issues and different possible application domains (like manufacturing, service management and trade systems) with their own specific needs and requirements. We invite both more academic and practical oriented submissions, but we are especially interested in academic research with a potential practical impact and practical research papers with theoretical implications. ICSBT 2024 received 27 paper submissions from 13 countries of which 14.8% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least 2 but usually 3 or more members of the International Program Committee, which is composed of established researchers and domain experts. The high quality of the ICSBT 2024 program is enhanced by the keynote lecture delivered by distinguished speakers who are renowned experts in their fields: Samuel Fosso Wamba (Toulouse Business School, France) and Sukhpal Singh Gill (Queen Many University of London, United Kingdom). All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index. As recognition for the best contributions, several awards based on the combined marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as assessed by session chairs at the conference venue, are conferred at the closing session of the conference. Authors of selected papers will be invited to submit extended versions for inclusion in a forthcoming book of ICSBT Selected Papers to be published by Springer, as part of the CCIS Series. Some papers will also be selected for publication of extended and revised versions in the special issue of the Socio-Economic Planning Sciences and IMA Journal of Management Mathematics. The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are herewith recorded. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly competent team. We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of ICSBT, details of which are available at https://icsbt.scitevents.org.

In today's competitive business environment, evaluating the performance of decision-making units (DMUs) such as countries and institutions is paramount. Data Envelopment Analysis (DEA) is widely used for this purpose. One prevalent model, the Distance Friction Minimization (DFM) method, is effective in devising improvement strategies for low-efficiency DMUs. However, it has limitations as it only assesses the distance of DMUs to the efficient frontier, neglecting the inefficient frontier and providing an overly optimistic assessment. Hence, there is a growing need for methods that consider both frontiers to overcome this issue. In this study, we introduce an enhanced DFM model that integrates both optimistic and pessimistic distance analyses. The research methodology is as follows: IDMU-based CCR and ADMU-based CCR models are designed and implemented to calculate the optimistic and pessimistic efficiency of DMUs, respectively. Then, additive models based on virtual IDMU and ADMU units are designed and implemented. Subsequently, DMUs in both approaches are categorized, and DMUs of the third category of each approach are entered into the respective DFM model. After calculating the distance of each DMU from both efficient and inefficient frontiers, the relative closeness (RC) index is employed to aggregate the distances of DMUs from the efficient and inefficient frontiers. Finally, the DMUs are ranked based on the RC index. To demonstrate the practicality of the model, we evaluate the sustainable performance of OECD countries concerning CO2 emissions. Our findings illustrate that the model can measure DMUs' distances to both efficient and inefficient frontiers, providing policymakers dealing with Sustainable Development Goals (SDGs) a more nuanced understanding of the situation. In summary, the DFM model proposed in this study bridges the gap by considering optimistic and pessimistic perspectives, offering a more comprehensive view of DMU performance. •The optimistic and pessimistic approach were extended to the Distance Friction Minimization (DFM) model;•Another feature was added to the DFM Model, incorporates the distance to both the efficient and inefficient frontiers;•A new DFM model was developed for ranking decision-making units;•A novel method was validated for measuring the sustainable performance of OECD countries considering CO2 emission;

The significant positive and negatives effects of transportation systems (TSs) on the sustainability of cities and human life draw much attention from both researchers and managers. Constructing bus rapid transit (BRT) networks, or adding new lines to the existing ones, is one of the cheapest and easiest solutions to improve the performance of the urban transportation network (UTN). Often, large number of candidate projects (BRT lines) renders the execution of all these projects impossible due to technical and financial limitations. Hence, evaluating the candidate projects and developing the best plan for constructing a BRT network is an important issue requiring a complex decision-making process. In this study, a multi-period triple-level sustainable BRT network design model has been proposed using data envelopment analysis, game theory, Malmquist Index (MI) and considering all sustainability dimensions including environment, economic and society. Both managers’ and passengers’ perspectives have been considered in the modeling. A procedure based on a genetic algorithm (GA) has been developed to solve the presented triple-level model. Finally, the model has been applied to a real-world case study of evaluating and selecting the BRT projects in the city of Isfahan, and the results have been analyzed.

Allocating a fixed cost among a set of peer decision-making units (DMUs) is one of the most important applications of data envelopment analysis. However, almost all existing studies have addressed the fixed cost allocation (FCA) problem within a traditional framework while ignoring the existence of undesirable outputs. Undesirable outputs are neither scarce in various production activities in real world applications nor trivial in efficiency evaluation and subsequent decision making. Motivated by this observation, this article attempts to explicitly extend the traditional FCA problem to situations in which DMUs are necessarily involved with undesirable outputs. To this end, we first investigate the efficiency evaluation of DMUs considering undesirable outputs based on the joint weak disposability assumption. Then, flexible FCA schemes are considered to revisit the efficiency evaluation process. The results show that feasible allocation schemes exist such that all DMUs can be simultaneously efficient. Furthermore, we define the comprehensive satisfaction degree and develop a satisfaction degree bargaining game approach to determine a unique FCA scheme. Finally, the proposed approach is tested with an empirical study of banking activities based on real conditions.

Data Envelopment Analysis (DEA) has been widely applied in measuring the efficiency of Decision-Making Units (DMUs). The conventional DEA has three major drawbacks: a) it does not consider Decision Makers’ (DMs) preferences in the evaluation process, b) DMUs in this model are flexible in weighting the criteria to reach the maximum possible efficiency, and c) it ignores the uncertainty in data. However, in many real-world applications, data are uncertain as well as imprecise and managers want to impose their opinions in decision-making procedure. To address these problems, this paper develops a novel multi-objective Best Worst Method (BWM)-Robust DEA (RDEA) for incorporating DMs’ preferences into DEA model in an uncertain environment. The proposed model tries to provide a new efficiency score which is more reliable and compatible with real problems by taking the advantages of the BWM to apply experts’ opinions and RDEA to model the uncertainty This bi-objective BWM-RDEA model is solved utilizing amin-max technique and so as to illustrate its usefulness, this model is implemented for assessing Iranian airlines.

The problem of assessment of Decision Making Units (DMUs) by using Data Envelopment Analysis (DEA) may not be straightforward due to the data uncertainty. Several studies have been developed to incorporate uncertainty into input/output values in the DEA literature. On the other hand, while traditional DEA models focus more on crisp data, there exist many applications in which data is reported in form of intervals. This paper considers the box-uncertainty in data which means that each input/output value is selected from a symmetric box. This specific type of uncertainty has been addressed as Interval DEA approaches. Our proposed model deals with efficiency evaluation of DMUs with imprecise data in a robust optimization. We assume that inputs and outputs are reported in the form of intervals and propose the robust counterpart problem for the envelopment form of the DEA model. Further, we also develop two ranking methods which have more benefits compared to some existing approaches. An illustrative example is provided to show how the proposed approaches work. An application on hospital efficiency in East Virginia is used to show the usefulness of the proposed approaches.

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

•Develop a novel Fuzzy Network DEA approach using adjustable possibilistic programming.•Reviews real-world applications of FNDEA literature.•The proposed fuzzy network DEA model is flexible, adjustable, general, and applicable.•The FNDEA method is capable to rank of Two-stage DMUs under fuzzy data.•The presented approach is applied to the performance evaluation of investment firms. This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence of imprecise and vague data. In order to achieve this goal, two-stage data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), and chance-constrained programming (CCP) are applied to propose the new fuzzy network data envelopment analysis (FNDEA) approach. The main advantages of the proposed FNDEA approach can be summarized as follows: linearity of the proposed FNDEA models, unique efficiency decomposing under ambiguity, capability to extending for other network structures. Moreover, FNDEA approach can be applied for ranking of two-stage DMUs under fuzzy environment in three stages: 1) solving the proposed FNDEA model for all optimistic-pessimistic viewpoints and confidence levels, 2) then plotting the results and drawing the surface of all efficiency scores, 3) and finally calculate the volume of the three-dimensional shape in below the efficiency surface. This volume can be as ranking criterion. Remarkably, the presented fuzzy network DEA approach is implemented for performance appraisal and ranking of investment firms (IFs) with two-stage processes including operational and portfolio management process. Illustrative results of the real-life case study show that the proposed approach is effective and practically very useful.

•Define a comprehensive sustainable urban transportation network (SUTN).•Evaluation criteria for SUTN based on economic, social and environmental factors.•Propose a framework based on Best Worst Method (BWM) to define a SUTN. A truly sustainable urban transportation network (SUTN) needs to be sustainable in all aspects including economic, social and environmental dimensions. Identifying the evaluation criteria for sustainability of urban transportation network (UTN) and evaluating importance of these criteria, are completely critical. While most researches have only focused on economic aspect of transportation systems (TSs), in this paper, by considering economic, social and environmental dimensions, the evaluation criteria for evaluating the sustainability of UTN have been identified. Then a framework based on Best Worst Method, has been proposed to evaluate and prioritize sustainability dimensions and evaluation criteria. To show the usefulness of the proposed model, it is applied to a real-world case study of transportation in Isfahan, one of the largest cities in Iran. The results from this study are used for evaluating and selecting real transportation projects. We have also shown how the proposed framework helps managers and experts for analyzing sustainability of existing UTN, identifying potential strategies, evaluating and selecting new policies or constructing projects to achieve sustainability goals.

This study suggests a novel application of Inverse Data Envelopment Analysis (InvDEA) in strategic decision making about mergers and acquisitions in banking. The conventional DEA assesses the efficiency of banks based on the information gathered about the quantities of inputs used to realize the observed level of outputs produced. The decision maker of a banking unit willing to merge/acquire another banking unit needs to decide about the inputs and/or outputs level if an efficiency target for the new banking unit is set. In this paper, a new InvDEA-based approach is developed to suggest the required level of the inputs and outputs for the merged bank to reach a predetermined efficiency target. This study illustrates the novelty of the proposed approach through the case of a bank considering merging with or acquiring one of its competitors to synergize and realize higher level of efficiency. A real data set of 42 banking units in Gulf Corporation Council countries is used to show the practicality of the proposed approach.

Traditional Data Envelopment Analysis (DEA) models find the most desirable weights for each Decision Making Unit (DMU) in order to estimate the highest efficiency score as possible. Usually, decision-makers are using these efficiency scores for ranking the DMUs. The main drawback in this process is that the ranking based on weights obtained from the standard DEA models ignore other feasible weights, this is due to the fact that DEA may have multiple solutions for each DMU. To overcome this problem, Salo and Punkka (2011) deemed each DMU as a "Black box" and developed a mix-integer model to obtain the ranking intervals for each DMU over sets of all its feasible weights. In many real-world applications, there are DMUs that have a two-stage production system. In this paper, we extend the Salo and Punkka (2011)'s model to more common and practical applications considering the two-stage production structure. The proposed approach calculates each DMU's ranking interval for the overall system as well as for each subsystem/sub-stage. An application for non-life insurance companies is given to illustrate the applicability of the proposed approach. A real application in Chinese commercial banks shows how this approach can be used by policy makers.

Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed. (C) 2016 Elsevier Ltd. All rights reserved.

This paper investigates the problem of efficiency measurement for parallel systems with two components based on Stackelberg game theory, while some inputs/outputs are fuzzy numbers. Conventional DEA models treat DMUs as “Black Boxes”. While in this paper, we propose a new parallel fuzzy DEA model to calculate the efficiency scores for each DMU’s whole system and its sub-systems. Through the Stackelberg (leader–follower) game theory, the whole system’s efficiency score of each DMU is decomposed into a set of efficiency scores for its sub-systems. This approach is independent of the α -cut which reduces the computational efforts. In order to show our method, we use the data from Beasley (J Oper Res Soc 46(4):441–452, 1995) to measure the fuzzy efficiency of the teaching and research efficiencies of chemistry departments in UK universities.

Group decision making is the study of identifying and selecting alternatives based on the values and preferences of the decision maker. Making a decision implies that there are several alternative choices to be considered. This paper uses the concept of Data Envelopment Analysis to introduce a new mathematical method for selecting the best alternative in a group decision making environment. The introduced model is a multi-objective function which is converted into a multi-objective linear programming model from which the optimal solution is obtained. A numerical example shows how the new model can be applied to rank the alternatives or to choose a subset of the most promising alternatives.

Integer-valued data envelopment analysis (DEA) with alternative returns to scale technology has been introduced and developed recently by Kuosmanen and Kazemi Matin. The proportionality assumption of their introduced "natural augmentability" axiom in constant and nondecreasing returns to scale technologies makes it possible to achieve feasible decision-making units (DMUs) of arbitrary large size. In many real world applications it is not possible to achieve such production plans since some of the input and output variables are bounded above. In this paper, we extend the axiomatic foundation of integer-valued DEA models for including bounded output variables. Some model variants are achieved by introducing a new axiom of "boundedness" over the selected output variables. A mixed integer linear programming (MILP) formulation is also introduced for computing efficiency scores in the associated production set.

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

This paper explores the use of the optimisation procedures in SAS/OR software with application to the measurement of efficiency and productivity of decision-making units (DMUs) using data envelopment analysis (DEA) techniques. DEA was originally introduced by Charnes et al. [J. Oper. Res. 2 (1978) 429] is a linear programming method for assessing the efficiency and productivity of DMUs. Over the last two decades, DEA has gained considerable attention as a managerial tool for measuring performance of organisations and it has widely been used for assessing the efficiency of public and private sectors such as banks, airlines, hospitals, universities and manufactures. As a result, new applications with more variables and more complicated models are being introduced. Further to successive development of DEA a non-parametric productivity measure, Malmquist index, has been introduced by Fare et al. [J. Prod. Anal. 3 (1992) 85]. Employing Malmquist index, productivity growth can be decomposed into technical change and efficiency change. On the other hand, the SAS is a powerful software and it is capable of running various optimisation problems such as linear programming with all types of constraints. To facilitate the use of DEA and Malmquist index by SAS users, a SAS/MALM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear-programming models based on the selected DEA. An example is given to illustrate how one could use the code to measure the efficiency and productivity of organisations.

Purpose The data used in this study is for the period 19802000. Almost midway through this period in 1992, the Kenyan government liberalized the sugar industry and the role of the market increased, while the government's role with respect to control of prices, imports and other aspects in the sector declined. This exposed the local sugar manufacturers to external competition from other sugar producers, especially from the COMESA region. This study aims to find whether there were any changes in efficiency of production between the two periods pre and postliberalization. Designmethodologyapproach The study utilized two methodologies to efficiency estimation data envelopment analysis DEA and the stochastic frontier. DEA uses mathematical programming techniques and does not impose any functional form on the data. However, it attributes all deviation from the mean function to inefficiencies. The stochastic frontier utilizes econometric techniques. Findings The test for structural differences in the two periods does not show any statistically significant differences between the two periods. However, both methodologies show a decline in efficiency levels from 1992, with the lowest period experienced in 1998. From then on, efficiency levels began to increase. Originalityvalue To the best of the authors' knowledge, this is the first paper to use both methodologies in the sugar industry in Kenya. It is shown that in industries where the noise error term is minimal such as manufacturing, the DEA and stochastic frontier give similar results.

This chapter provides the theoretical foundation and background on data envelopment analysis (DEA) method. We first introduce the basic DEA models. The balance of this chapter focuses on evidences showing DEA has been extensively applied for measuring efficiency and productivity of services including financial services (banking, insurance, securities, and fund management), professional services, health services, education services, environmental and public services, energy services, logistics, tourism, information technology, telecommunications, transport, distribution, audio-visual, media, entertainment, cultural and other business services. Finally, we provide information on the use of Performance Improvement Management Software (PIM-DEA). A free limited version of this software and downloading procedure is also included in this chapter.

Many production systems have acquisition and merge operations to increase productivity. This paper proposes a novel method to anticipate whether a merger in a market is generating a major or a minor consolidation, using Inverse data envelopment analysis (InvDEA) model. A merger between two or more decision making units (DMUs) producing a single merged DMU that affects the efficiency frontier, defined by the pre-consolidation market conditions, is called a major consolidation. The corresponding alternative case is called a minor consolidation. A necessary and sufficient condition to distinguish the two types of consolidations is proven and two numerical illustrations in banking and supply chain management are discussed. The crucial importance of anticipating the magnitude of a consolidation in a market is outlined. (C) 2016 Elsevier Ltd. All rights reserved.

Since the original Data Envelopment Analysis (DEA) study by Charnes et al. [Measuring the efficiency of decision-making units. European Journal of Operational Research 1978;2(6):429–44], there has been rapid and continuous growth in the field. As a result, a considerable amount of published research has appeared, with a significant portion focused on DEA applications of efficiency and productivity in both public and private sector activities. While several bibliographic collections have been reported, a comprehensive listing and analysis of DEA research covering its first 30 years of history is not available. This paper thus presents an extensive, if not nearly complete, listing of DEA research covering theoretical developments as well as “real-world” applications from inception to the year 2007. A listing of the most utilized/relevant journals, a keyword analysis, and selected statistics are presented.

The 4th International Conference on Computer Science and Application Engineering (CSAE 2020) will be held in virtual during October 20-22, 2020 coz COVID-19 Pandemic. It is held annually to provide a comprehensive global forum for experts and participants from academia to exchange ideas and present results of ongoing research in the most state-of-the-art areas of computer science and application engineering.

Data envelopment analysis (DEA) is a non-parametric optimization approach that was first introduced by Charnes et al. (1978) and is widely used for assessing the performance and comparative efficiency of decision-making units (DMUs) in both public and private sectors. It has emerged as a success story of management science and has found applications in various domains, including environmental, banking, healthcare, transportation, education, manufacturing, agriculture, energy, sport, and tourism. DEA's popularity has grown rapidly since its inception, and it continues to be a valuable tool for decision-makers in various fields (Emrouznejad et al.; 2018). Standard DEA models evaluate the relative efficiency of DMUs based on their input and output data, but they do not provide information on estimating the amount of inputs and/or outputs needed to achieve efficiency targets. To determine these data, an inverse DEA model must be solved. This requires the development of appropriate mathematical models that are capable of solving the associate inverse problems. Wei et al. (2000) and Amin et al. (2017) highlighted the importance of solving inverse DEA problems and contributed to the development of related mathematical models. However, the challenge of solving inverse DEA problems is still an ongoing research area, and there is a need for

Data Envelopment Analysis (DEA) is a widely used mathematical programming approach for assessing the efficiency of decision-making units (DMUs) in various sectors. Inverse DEA is a post-DEA sensitivity analysis approach developed initially for solving resource allocation. The main objective of Inverse DEA is to determine the optimal quantity of inputs and/or outputs for each DMU under input and/or output perturbation(s) that would allow them to reach a given efficiency target. Since the early 2000s, Inverse DEA has been extended theoretically and applied successfully in different areas including banking, energy, education, sustainability, and supply chain management. In recent years, research has demonstrated the potential of Inverse DEA for solving novel inverse problems, such as estimating merger gains, minimizing production pollution, optimizing business partnerships, and more. This paper provides a comprehensive survey of the latest theoretical and practical advancements in Inverse DEA, while also highlighting potential areas for future research and development in this field. One such area is exploring the use of heuristic algorithms and optimization techniques in conjunction with Inverse DEA models to address issues of infeasibility and a.emrouznejad@surrey.ac.uk 2 nonlinearity. Moreover, applying Inverse DEA to new sectors such as healthcare, agriculture, and environmental and climate change issues holds great promise for future research. Overall, this paper sets the stage for further advancements in this promising approach.

In most of the analyses employed where DEA is applied, the need to assess productivity for a given period of time is often necessary. Since classical DEA formulations ignore the temporal dimension of data, new DEA formulations and relevant indices have been proposed. Among them, the most important is the Malmquist Productivity Index or MPI. This index calculates the productivity change of the DMUs between two consecutive years t and t + 1. Because of this, the data should also include the time information for each DMU’s inputs and outputs.

Aside from the classical DEA models for assessing efficiency, special models based on either the DEA technique or similar functioning have been proposed over the years. In this chapter, we will focus on the Benefit-of-the-Doubt model and the Multi-objective Linear Programming in DEA.

Emrouznejad et al. (2010) proposed a Semi-Oriented Radial Measure (SORM) model for assessing the efficiency of Decision Making Units (DMUs) by Data Envelopment Analysis (DEA) with negative data. This paper provides a necessary and sufficient condition for boundedness of the input and output oriented SORM models.

This paper proposes a new method to evaluate decision-making units (DMUs) under uncertainty using fuzzy data envelopment analysis (DEA). In the proposed multi-objective nonlinear programming methodology, both the objective functions and the constraints are considered fuzzy. This model is comprehensive in dealing with uncertainty, in the sense that coefficients of the decision variables in the objective functions and in the constraints, as well as the DMUs under assessment, are assumed to be fuzzy numbers with triangular membership functions. A comparison between the current fuzzy DEA models and the proposed method is illustrated by a numerical example.

In recent years, environmental problems caused by industries in China have drawn increasing attention to both academics and policy makers. This paper assesses the environmental efficiency of Chinese regional industrial systems to come up with some recommendations to policy makers. First, we divided each Chinese regional industrial system into a production process and a pollutant treatment process. Then, we built a scientific input-intermediate-output index system by introducing a new network slacks-based model (NSBM) model. This study is the first to combine NSBM with DEA window analysis to give a dynamic evaluation of the environmental efficiency. This enables us to assess the environmental efficiency of Chinese regional industrial systems considering their internal structure as well as China's policies concerning resource utilization and environmental protection. Hence, the overall efficiency of each regional industrial system is decomposed into production efficiency and pollutant treatment efficiency. Our empirical results suggest: (1) 66.7% of Chinese regional industrial systems are overall inefficient. 63.3 and of 66.7% Chinese regional industrial systems are inefficient in the production process and the pollutant treatment process, respectively. (2) The efficiency scores for the overall system and both processes are all larger in the eastern area of China than those of the central and western areas. (3) Correlation analysis indicates that SO2 generation intensity (SGI), solid waste generation intensity, COD discharge intensity, and SO2 discharge intensity have significantly negative impacts on the overall efficiency. (4) The overall inefficiency is mainly due to inefficiency of the pollutant treatment process for the majority of regional industrial systems. (5) In general, the overall efficiency was trending up from 2004 to 2010, indicating that the substantial efforts China has devoted to protecting the environment have yielded benefits.

Using panel data for 52 developed and developing countries over the period 1998-2006, this article examines the links between information and communication technology diffusion and human development. We conducted a panel regression analysis of the investments per capita in healthcare, education and information and communication technology against human development index scores. Using a quantile regression approach, our findings suggest that changes in healthcare, education and information and communication technology provision have a stronger impact on human development index scores for less developed than for highly developed countries. Furthermore, at lower levels of development education fosters development directly and also indirectly through their enhanced effects on ICT. At higher levels of development education has only an indirect effect on development through the return to ICT.

Assessing employee performance is one of the most important issue in healthcare management services. Because of their direct relationship with patients, nurses are also the most influential hospital staff who play a vital role in providing healthcare services. In this paper, a novel Data Envelopment Analysis Matrix (DEAM) approach is proposed for assessing the performance of nurses based on relative efficiency. The proposed model consists of five input variables (including type of employment, work experience, training hours, working hours and overtime hours) and eight output variables (the outputs are amount of hours each nurse spend on each of the eight activities including documentation, medical instructions, wound care and patient drainage, laboratory sampling, assessment and control care, follow-up and counseling and para-clinical measures, attendance during visiting and discharge suction) have been tested on 30 nurses from the heart department of a hospital in Iran. After determining the relative efficiency of each nurse based on the DEA model, the nurses' performance were evaluated in a DEAM format. As results the nurses were divided into four groups; superstars, potential stars, those who are needed to be trained effectively and question marks. Finally, based on the proposed approach, we have drawn some recommendations to policy makers in order to improve and maintain the performance of each of these groups. The proposed approach provides a practical framework for hospital managers so that they can assess the relative efficiency of nurses, plan and take steps to improve the quality of healthcare delivery.

This paper examines the problems in the definition of the General Non-Parametric Corporate Performance (GNCP) and introduces a multiplicative linear programming as an alternative model for corporate performance. We verified and tested a statistically significant difference between the two models based on the application of 27 UK industries using six performance ratios. Our new model is found to be a more robust performance model than the previous standard Data Envelopment Analysis (DEA) model.

Conventional DEA models assume deterministic, precise and non-negative data for input and output observations. However, real applications may be characterized by observations that are given in form of intervals and include negative numbers. For instance, the consumption of electricity in decentralized energy resources may be either negative or positive, depending on the heat consumption. Likewise, the heat losses in distribution networks may be within a certain range, depending on e.g. external temperature and real-time outtake. Complementing earlier work separately addressing the two problems; interval data and negative data; we propose a comprehensive evaluation process for measuring the relative efficiencies of a set of DMUs in DEA. In our general formulation, the intervals may contain upper or lower bounds with different signs. The proposed method determines upper and lower bounds for the technical efficiency through the limits of the intervals after decomposition. Based on the interval scores, DMUs are then classified into three classes, namely, the strictly efficient, weakly efficient and inefficient. An intuitive ranking approach is presented for the respective classes. The approach is demonstrated through an application to the evaluation of bank branches.

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA. (c) 2020 China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The concept of ordered weighted averaging (OWA) operator weights arises in uncertain decision making problems, however some weights may have a specific relationship with other. This information about the weights can be obtained from decision makers (DMs). This paper intends to introduce a theory of weight restrictions into the existing OWA operator weight models. Based on the DMs' value judgment the obtained OWA operator weights could be more realistic.

Two-stage data envelopment analysis (DEA) efficiency models identify the efficient frontier of a two-stage production process. In some two-stage processes, the inputs to the first stage are shared by the second stage, known as shared inputs. This paper proposes a new relational linear DEA model for dealing with measuring the efficiency score of two-stage processes with shared inputs under constant returns-to-scale assumption. Two case studies of banking industry and university operations are taken as two examples to illustrate the potential applications of the proposed approach.

In practical term any result obtained using an ordered weighted averaging (OWA) operator heavily depends upon the method to determine the weighting vector. Several approaches for obtaining the associated weights have been suggested in the literature, in which none of them took into account the preference of alternatives. This paper presents a method for determining the OWA weights when the preferences of alternatives across all the criteria are considered. An example is given to illustrate this method and an application in internet search engine shows the use of this new OWA operator.

One of the main objectives in restructuring power industry is enhancing the efficiency of power facilities. However, power generation industry, which plays a key role in the power industry, has a noticeable share in emission amongst all other emission-generating sectors. In this study, we have developed some new Data Envelopment Analysis models to find efficient power plants based on less fuel consumption, combusting less polluting fuel types, and incorporating emission factors in order to measure the ecological efficiency trend. We then applied these models to measuring eco-efficiency during an eight-year period of power industry restructuring in Iran. Results reveal that there has been a significant improvement in eco-efficiency, cost efficiency and allocative efficiency of the power plants during the restructuring period. It is also shown that despite the hydro power plants look eco-efficient; the combined cycle ones have been more allocative efficient than the other power generation technologies used in Iran.

•To introduce an Integrated Fuzzy Clustering Cooperative Game DEA.•To provide a clustering technique to deal with lack of homogeneity among DMUs.•To provide a framework for measuring hospitals in different provinces.•Use of Core and Shapley values for ranking efficient DMUs in DEA. Hospitals are the main sub-section of health care systems and evaluation of hospitals is one of the most important issue for health policy makers. Data Envelopment Analysis (DEA) is a nonparametric method that has recently been used for measuring efficiency and productivity of Decision Making Units (DMUs) and commonly applied for comparison of hospitals. However, one of the important assumption in DEA is that DMUs must be homogenous. The crucial issue in hospital efficiency is that hospitals are providing different services and so may not be comparable. In this paper, we propose an integrated fuzzy clustering cooperative game DEA approach. In fact, due to the lack of homogeneity among DMUs, we first propose to use a fuzzy C-means technique to cluster the DMUs. Then we apply DEA combined with the game theory where each DMU is considered as a player, using Core and Shapley value approaches within each cluster. The procedure has successfully been applied for performances measurement of 288 hospitals in 31 provinces of Iran. Finally, since the classical DEA model is not capable to distinguish between efficient DMUs, efficient hospitals within each cluster, are ranked using combined DEA model and cooperative game approach. The results show that the Core and Shapley values are suitable for fully ranking of efficient hospitals in the healthcare systems.

Conventional Data Envelopment Analysis (DEA) assesses the performance of Decision-Making Units (DMUs) by measuring only one type of efficiency using definite data. However, due to the vastness of some industries, such as banks, calculating only one type of efficiency cannot confirm DMUs as efficient or inefficient. Also, real-world cases are often faced with uncertain data, and conventional DEA lacks the power to consider these uncertainties in evaluation. In this paper, we develop a multi-objective DEA model to calculate three types of efficiencies, including profitability, operational, and transactional for bank branches when there are uncertain data. First, we apply a modified DEA model which is capable of calculating the common weight of all inputs and outputs by solving only one linear programming model. Then, we employ a robust approach to handle the uncertainty in data. The uncertainty in data is described with discrete scenarios. Discrete scenarios require a set of possible values for each parameter with uncertain nature. Finally, we apply a fuzzy programming method to convert the proposed multi-objective model into a single-objective one. Our main goal is to calculate three types of efficiencies for bank branches under four different scenarios. To validate the accuracy of the proposed model, a real case of 45 Agriculture bank branches located in West Azerbaijan in Iran is examined. The results show that the proposed model can produce accurate results under different scenarios. We also perform a comparative analysis on each efficiency aspect to specify the benchmark branches and also inefficient branches. Comparative analysis can help managers recognize where improvement should be prioritized.

This paper proposes a new slacks-based measure network data envelopment analysis (SBM-NDEA) model with undesirable outputs to evaluate the performance of production processes that have complex structure containing both series and parallel processes. We demonstrate the proposed approach by evaluating Chinese commercial banks during 2012-2016. The operational process of these banks could be divided into deposit producing and deposit utilizing processes connected serially, while deposit utilizing process is further divided into profit generating and deposit reserve interest earning processes, which are parallel. The overall efficiency is decomposed into deposit producing and deposit utilizing efficiency. Deposit utilizing efficiency is further decomposed into profit generating and deposit reserve interest earning efficiency, respectively. Our empirical results suggest that the overall inefficiency is mainly from the profit generating process. The results also estimate the adjustment of variables for the network process of an inefficient bank.

In the majority of production processes, noticeable amounts of bad byproducts or bad outputs are produced. The negative effects of the bad outputs on efficiency cannot be handled by the standard Malmquist index to measure productivity change over time. Toward this end, the Malmquist-Luenberger index (MLI) has been introduced, when undesirable outputs are present In this paper, we introduce a Data Envelopment Analysis (DEA) model as well as an algorithm, which can successfully eliminate a common infeasibility problem encountered in MLI mixed period problems. This model incorporates the best endogenous direction amongst all other possible directions to increase desirable output and decrease the undesirable outputs at the same time. A simple example used to illustrate the new algorithm and a real application of steam power plants is used to show the applicability of the proposed model. (C) 2014 Elsevier Ltd. All rights reserved.

The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics. The change in attitude towards energy consumption behavior was analyzed to evaluate the impact of smart meter feedback as well. The key finding was 81% of trial participants perceived smart meters to be helpful in reducing their energy consumption. In addition, we found that the potential to reduce energy bills and environmental concerns were the strongest motivations for behavior change.

Data envelopment analysis (DEA) is one of the most widely used tools in efficiency analysis of many business and non-profit organisations. Recently, more and more researchers investigated DEA models without explicit input (DEA-WEI). DEA-WEI models can divide DMUs into two categories: efficient DMUs and inefficient DMUs. Usually there is a set of DMUs, which are 'efficient' so that conventional DEA models could not rank them. In this paper, we first develop a performance index based on efficient and anti-efficient frontiers in DEA-WEI models. Further, the corresponding performance index in DEA-WEI models with quadratic utility terms (quadratic DEA-WEI) is proposed also. Finally, we present two case studies on performance assessment of basketball players and the evaluation of research institutes in Chinese Academy of Sciences (CAS) to show the applicability and usefulness of the performance indices developed in this paper.

The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and hence slow down the epidemic spread. Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperforms the others. Extensive simulations using several optimization schemes show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.

This paper explains some drawbacks on previous approaches for detecting influential observations in deterministic nonparametric data envelopment analysis models as developed by Yang et al. (Annals of Operations Research 173:89-103, 2010). For example efficiency scores and relative entropies obtained in this model are unimportant to outlier detection and the empirical distribution of all estimated relative entropies is not a Monte-Carlo approximation. In this paper we developed a new method to detect whether a specific DMU is truly influential and a statistical test has been applied to determine the significance level. An application for measuring efficiency of hospitals is used to show the superiority of this method that leads to significant advancements in outlier detection.

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. In this study, we provide a taxonomy and review of the fuzzy DEA methods. We present a classification scheme with four primary categories, namely, the tolerance approach, the alpha-level based approach, the fuzzy ranking approach and the possibility approach. We discuss each classification scheme and group the fuzzy DEA papers published in the literature over the past 20 years. To the best of our knowledge, this paper appears to be the only review and complete source of references on fuzzy DEA. (C) 2011 Elsevier B.V. All rights reserved.

When a query is passed to multiple search engines, each search engine returns a ranked list of documents. Researchers have demonstrated that combining results, in the form of a "metasearch engine", produces a significant improvement in coverage and search effectiveness. This paper proposes a linear programming mathematical model for optimizing the ranked list result of a given group of Web search engines for an issued query. An application with a numerical illustration shows the advantages of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.

•A fixed cost allocation model for a two-stage system is built.•It is first work to apply efficiency invariance principle in two-stage system.•Both cooperative and non-cooperative scenarios are investigated.•The former scenarios are based on overall efficiency invariance principle.•The latter scenarios are based on divisional efficiency invariance principle. Fixed cost allocation among groups of entities is a prominent issue in numerous organisations. Addressing this issue has become one of the most important topics of the data envelopment analysis (DEA) methodology. In this study, we propose a fixed cost allocation approach for basic two-stage systems based on the principle of efficiency invariance and then extend it to general two-stage systems. Fixed cost allocation in cooperative and noncooperative scenarios are investigated to develop the related allocation plans for two-stage systems. The model of fixed cost allocation under the overall condition of efficiency invariance is first developed when the two stages have a cooperative relationship. Then, the model of fixed cost allocation under the divisional condition of efficiency invariance wherein the two stages have a noncooperative relationship is studied. Finally, the validation of the proposed approach is demonstrated by a real application of 24 nonlife insurance companies, in which a comparative analysis with other allocation approaches is included.

Purpose - A binary integer programming model for the simple assembly line balancing problem (SALBP), which is well known as SALBP-1, was formulated more than 30 years ago, Since then, a number of researchers have extended the model for the variants of assembly line balancing problem. The model is still prevalent nowadays mainly because of the lower and upper bounds on task assignment. These properties avoid significant increase of decision variables. The purpose of this paper is to use an example to show that the model may lead to a confusing solution. Design/methodology/approach - The paper provides a remedial constraint set for the model to rectify the disordered sequence problem. Findings - The paper presents proof that the assembly line balancing model formulated by Patterson and Albracht may lead to a confusing solution. Originality/value - No one previously has found that the commonly used model is incorrect.

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named "efficiency cuts", selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples.

•We propose new Fuzzy-DEA α-level models to assess data uncertainty.•Bootstrap truncated regressions are used to identify relevant variables on efficiency.•Proposed models have been demonstrated using an application in Mozambican banks.•Findings reveal that fuzziness is predominant over randomness in the results.•Price of labor, price of capital, and market-share were found to be significant factors. Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we propose new Fuzzy-DEA α-level models to assess underlying uncertainty. Further, bootstrap truncated regressions with fixed factors are used to measure the impact of each model on the efficiency scores and to identify the most relevant contextual variables on efficiency. The proposed models have been demonstrated using an application in Mozambican banks to handle the underlying uncertainty. Findings reveal that fuzziness is predominant over randomness in interpreting the results. In addition, fuzziness can be used by decision-makers to identify missing variables to help in interpreting the results. Price of labor, price of capital, and market-share were found to be the significant factors in measuring bank efficiency. Managerial implications are addressed.

In this paper, we reformulate the conventional DEA models as an imprecise DEA problem and propose a novel method for evaluating the DMUs when the inputs and outputs are fuzzy and/or ordinal or vary in intervals. For this purpose, we convert all data into interval data. In order to convert each fuzzy number into interval data, we use the nearest weighted interval approximation of fuzzy numbers by applying the weighting function, and we convert each ordinal data into interval one. In this manner, we could convert all data into interval data. The presented models determine the interval efficiencies for DMUs. To rank DMUs based on their associated interval efficiencies, we first apply the ohm-index that is developed for ranking of interval numbers. Then, by introducing an ideal DMU, we rank efficient DMUs to present a complete ranking. Finally, we use one example to illustrate the process and one real application in health care to show the usefulness of the proposed approach. For this evaluation, we consider interval, ordinal, and fuzzy data alongside the precise data to evaluate 38 hospitals selected by OIG. The results reveal the capabilities of the presented method to deal with the imprecise data.

Middle East and North Africa (MENA) countries present a banking industry that is well-known for regulatory and cultural heterogeneity, besides ownership, origin, and type diversity. This paper explores these issues by developing a Dynamic Network DEA model in order to handle the underlying relationships among major accounting and financial indicators. Firstly, a relational model encompassing major profit sheet, balance sheet, and financial health indicators is presented under a dynamic network structure. Subsequently, the dynamic effect of carry-over indicators is incorporated into it so that efficiency scores can be properly computed for these three substructures. The impact of contextual variables related to bank ownership, its type, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non-linear model solved by differential evolution, which combines bootstrapped Simplex, Tobit, Beta, and Simar and Wilson truncated regression results. The results reveal that bank type, origin, and ownership impact efficiency levels differently in terms of profit sheet, balance sheet, and financial health indicators, although the impact of culture and regulatory barriers seem to prevail at the country level.

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.

Linear programming (LP) is the most widely used optimization technique for solving real-life problems because of its simplicity and efficiency. Although conventional LP models require precise data, managers and decision makers dealing with real-world optimization problems often do not have access to exact values. Fuzzy sets have been used in the fuzzy LP (FLP) problems to deal with the imprecise data in the decision variables, objective function and/or the constraints. The imprecisions in the FLP problems could be related to (1) the decision variables; (2) the coefficients of the decision variables in the objective function; (3) the coefficients of the decision variables in the constraints; (4) the right-hand-side of the constraints; or (5) all of these parameters. In this paper, we develop a new stepwise FLP model where fuzzy numbers are considered for the coefficients of the decision variables in the objective function, the coefficients of the decision variables in the constraints and the right-hand-side of the constraints. In the first step, we use the possibility and necessity relations for fuzzy constraints without considering the fuzzy objective function. In the subsequent step, we extend our method to the fuzzy objective function. We use two numerical examples from the FLP literature for comparison purposes and to demonstrate the applicability of the proposed method and the computational efficiency of the procedures and algorithms.

This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach.

Background: The aim of this study was to suggest a suitable context to develop efficient hospital systems while maintaining the quality of care at minimum expenditures. Methods: This research aimed to present a model of efficiency for selected public and private hospitals of East Azerbaijani Province of Iran by making use of Data Envelopment Analysis approach in order to recognize and suggest the best practice standards. Results: Among the six inefficient hospitals, 2 (33%) had a technical efficiency score of less than 50% (both private), 2 (33%) between 51 and 74% (one private and one public) and the rest (2, 33%) between 75 and 99% (one private and one public). Conclusion: In general, the public hospitals are relatively more efficient than private ones; it is recommended for inefficient hospitals to make use of the followings: transferring, selling, or renting idle/unused beds; transferring excess doctors and nurses to the efficient hospitals or other health centers; pensioning off, early retirement clinic officers, technicians/technologists, and other technical staff. The saving obtained from the above approaches could be used to improve remuneration for remaining staff and quality of health care services of hospitals, rural and urban health centers, support communities to start or sustain systematic risk and resource pooling and cost sharing mechanisms for protecting beneficiaries against unexpected health care costs, compensate the capital depreciation, increasing investments, and improve diseases prevention services and facilities in the provincial level.

Data envelopment analysis is a relative performance assessment method to evaluate performance of a group of decision making units. Empirically, when the number of decision making units is insufficient, the classical data envelopment analysis models cannot discriminate the efficient units perfectly. To overcome this issue, in this paper, several mathematical approaches, including “multivariate data analysis techniques”, “game theory”, “Shannon entropy” and “the technique for order of preference by similarity to ideal solution”, are combined with data envelopment analysis. The proposed framework is applied to evaluate performance of Iranian thermal power plants. Inefficient performance of thermal power plants may end up in serious economic and environmental problems for example CO2 emission. Therefore, evaluating performance of thermal power plants and identifying their weaknesses in order to improve their performance is a necessity. The obtained results are analyzed, and some practical suggestions are provided to achieve sustainable performance and a cleaner production system. •Multistage data analysis and game theory are combined with DEA to overcome the problem insufficient number of DMUs.•An approach is proposed to integrate the results of different DEA models.•The proposed approaches are applied to evaluate the performance of Iranian thermal power plants.•The power plants should move from governmental structure toward becoming private.•New cleaner production systems should be installed.

This paper proposes an allocation Malmquist index which is inspired by the work on the non-parametric cost Malmquist index. We first show that how to decompose the cost Malmquist index into the input-oriented Malmquist index and the allocation Malmquist index. An application in corporate management of the China securities industry with the panel data set of 40 securities companies during the period 2005-2011 shows the practicality of the propose model.

Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.

Economic crisis and uncertainty in global status quo affect stock markets around the world. This fact imposes improvement in the development of volatility models. However, the comparison among volatility models cannot be made based on a single-error measure as a model can perform better in one-error measure and worst in another. In this paper, we propose a two-stage approach for prioritizing volatility models, where in the first stage we develop a novel slack-based data envelopment analysis to rank volatility models. The robustness of the proposed approach has also been investigated using cluster analysis. In the second-stage analysis, it is investigated whether the efficiency scores depend on model characteristics. These attributes concern the time needed in order to estimate the model, the value of Akaike Information Criterion, the number of models' significant parameters, groups and bias terms, and the error sum of squares (ESS). Further, dummy variables have been introduced to the regression model in order to find whether the employed model includes an in-mean effect, whether the assumed distribution is skewed, and whether the employed model belongs to the generalized autoregressive conditional heteroskedasticity (GARCH) family. The main findings of this research show that the number of models' statistically significant coefficients, ESS, and in-mean effects tend to increase the efficiency scores, while time elapsed, the number of statistically significant bias terms, and skewed error distributions tend to decrease the efficiency score.

Global warming, climate change, and social problems are the worst human-induced sustainability issues that economies across the globe have witnessed. Water pollution, greenhouse effect, poor working conditions, child labour and lack of coordination among channel partners have caused the considerable interruptions in the supply chain network. The purpose of the paper is to identify critical factors affecting behavioural and sustainable supply chain coordination and evaluate strategies for risk reduction in the supply chain coordination in the context of digitization. This study purposes a novel supply chain coordination framework which consists of four themes such as system, actor, objective and action on which the success or the failure of supply chain can be contingent. Our study integrates multi-criteria decision approach using Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to investigate factors that affected the behavioural and sustainable supply chain coordination in the context of digitization. The Fuzzy-AHP method qualified to hierarchically rank the factors based on the relative fuzzy weightage while Fuzzy-DEMATEL established the interrelationships among the factors and classified them into cause and effect groups. The findings of our study identified the Environmental performance and decarbonization as the most significant factor and the speed to market as the least important factor in developing behavioural and sustainable supply chain coordination in the context of digitization. Our analysis from Fuzzy AHP-DEMATEL approach reveal that the social preferences (power balance, reciprocity, fairness) is a significant causal factor which can effectively abolish the issues plaguing behavioural and sustainable supply chain coordination in the context of digitization. The results from our study aim to facilitate decision makers in cultivating a sustainable supply chain framework that can boost trust among the channel partners environmental performance, social performance and channel efficiency of the supply chain, thereby ensuring sustainability and socio welfare of all the supply chain.

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This chapter provides a taxonomy and review of the fuzzy DEA (FDEA) methods. We present a classification scheme with six categories, namely, the tolerance approach, the a-level based approach, the fuzzy ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy random/type-2 fuzzy set. We discuss each classification scheme and group the FDEA papers published in the literature over the past 30 years.

This paper develops an integrated approach, combining quality function deployment (QFD), fuzzy set theory, and analytic hierarchy process (AHP) approach, to evaluate and select the optimal third-party logistics service providers (3PLs). In the approach, multiple evaluating criteria are derived from the requirements of company stakeholders using a series of house of quality (HOQ). The importance of evaluating criteria is prioritized with respect to the degree of achieving the stakeholder requirements using fuzzy AHP. Based on the ranked criteria, alternative 3PLs are evaluated and compared with each other using fuzzy AHP again to make an optimal selection. The effectiveness of proposed approach is demonstrated by applying it to a Hong Kong based enterprise that supplies hard disk components. The proposed integrated approach outperforms the existing approaches because the outsourcing strategy and 3PLs selection are derived from the corporate/business strategy. (C) 2012 Elsevier Ltd. All rights reserved.

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency and productivity of decision-making units (DMUs). On the other hand data mining techniques allow DMUs to explore and discover meaningful, previously hidden information from large databases. Classification and regression (C&R) is the commonly used decision tree in data mining. DEA determines the efficiency scores but cannot give details of factors related to inefficiency, especially if these factors are in the form of non-numeric variables such as operational style in the banking sector. This paper proposes a framework to combine DEA with C&R for assessing the efficiency and productivity of DMUs. The result of the combined model is a set of rules that can be used by policy makers to discover reasons behind efficient and inefficient DMUs. As a case study, we use the proposed methodology to investigate factors associated with the efficiency of the banking sector in the Gulf Cooperation Council countries.

Purpose The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system. Design/methodology/approach The current study utilizes data envelopment analysis (DEA) to develop a combined queuing-DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring. Findings The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the "required" number of personnel, corresponding to different wait times, indicating queue build-up. Originality/value The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where "all" patients are walk-in and no appointment systems. This model provides vital information in the form of "required" number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.

•This paper explores influence of IT investment on hospital efficiency and quality.•There is a direct effect of IT investment on service quality in hospitals.•There is a moderating effect of quality on operational efficiency in hospitals.•There is a U-shaped relationship between IT investments and operational efficiency.•IT investments have diminishing returns beyond a certain point. The influence of IT investment on hospital efficiency and quality are of great interest to healthcare executives as well as insurers. Few studies have examined how IT investments influence both efficiency and quality or whether there is an optimal IT investment level that influences both in the desired direction. Decision makers in healthcare wonder if there are tradeoffs between their pursuit of hospital operational efficiency and quality. Our study involving a 2-stage double bootstrap DEA analysis of 187 US hospitals over 2years found direct effects of IT investment upon service quality and a moderating effect of quality upon operational efficiency. Further, our findings indicate a U-shaped relationship between IT investments and operational efficiency suggesting that IT investments have diminishing returns beyond a certain point.

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers. (C) 2016 Elsevier B.V. All rights reserved.

Neuron reconstruction algorithms used in electron microscope volumes have received increasing attention in recent years. Most current methods are highly reliant on neuron membrane boundary evidence without considering biological plausibility. In this investigation, we present a novel neuron reconstruction framework via the fusion of a global optimization goal and biologically inspired priors. We encode the 3D instances of synapses and mitochondria as two types of constraints to allow for the direct inclusion of non-local connectivity information in the neuron segmentation. Moreover, a flexible decision procedure is designed to retain high -confidence priors to deal with the possible influence of the upstream ultrastructure error. We construct the constrained graph partitioning model and adapt two greedy algorithms with the polynomial time complexity to solve the proposed model. We perform comparative studies on several public datasets and demonstrate that the decision of ultrastructural connectivity constraints contributes to significant improvements over existing hierarchical agglomeration algorithms. The ablation studies of ultrastructures from different recognition accuracy suggest the generality and applicability of the proposed method.

This paper seeks to advance the theory and practice of the dynamics of complex networks in relation to direct and indirect citations. It applies social network analysis (SNA) and the ordered weighted averaging operator (OWA) to study a patent citations network. So far the SNA studies investigating long chains of patents citations have rarely been undertaken and the importance of a node in a network has been associated mostly with its number of direct ties. In this research OWA is used to analyse complex networks, assess the role of indirect ties, and provide guidance to reduce complexity for decision makers and analysts. An empirical example of a set of European patents published in 2000 in the renewable energy industry is provided to show the usefulness of the proposed approach for the preference ranking of patent citations. Crown Copyright (C) 2015 Published by Elsevier Inc. All rights reserved.

Distributing loan using group lending method is one of the unique features in microfinance, as it utilises peer monitoring and dynamic incentive to lower credit risks in extending collateral-free loan to the poor. However, many microfinance institutions (MFIs) eventually perceive it to be costly and restricting loan growth thereby resorted to individual lending method to enhance profitability. On the other hand, village banking method was developed to boost outreach and to create self-sustaining village microbanks. We thus seek to empirically observe the loan method- efficiency relationship and to examine the best loan method regionally; focusing on not-for-profit MFIs that are widely regarded as best microfinance provider. Non-oriented Data Envelopment Analysis with regional meta-frontier approach is used for efficiency assessment of 628 MFIs from 87 countries in 6 regions, followed by Tobit regression. We also investigated factors affecting efficiencies such as borrowings, total donation, cost per borrower (CPB), portfolio at risk (PAR), interest rates, MFI age, regulation status, and legal format. The results support our argument that appropriate performance analysis should best be performed on regional basis separately as we find different results for different region. Crown Copyright (C) 2017 Published by Elsevier Ltd.

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This chapter provides a taxonomy and review of the fuzzy DEA (FDEA) methods. We present a classification scheme with six categories, namely, the tolerance approach, the α-level based approach, the fuzzy ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy random/type-2 fuzzy set. We discuss each classification scheme and group the FDEA papers published in the literature over the past 30 years.

Zambia and many other countries in Sub-Saharan Africa face a key challenge of sustaining high levels of coverage of AIDS treatment under prospects of dwindling global resources for HIV/AIDS treatment. Policy debate in HIV/AIDS is increasingly paying more focus to efficiency in the use of available resources. In this chapter, we apply Data Envelopment Analysis (DEA) to estimate short term technical efficiency of 34 HIV/AIDS treatment facilities in Zambia. The data consists of input variables such as human resources, medical equipment, building space, drugs, medical supplies, and other materials used in providing HIV/AIDS treatment. Two main outputs namely, numbers of ART-years (Anti-Retroviral Therapy-years) and pre-ART-years are included in the model. Results show the mean technical efficiency score to be 83 %, with great variability in efficiency scores across the facilities. Scale inefficiency is also shown to be significant. About half of the facilities were on the efficiency frontier. We also construct bootstrap confidence intervals around the efficiency scores.

This study uses Data Envelopment Analysis (DEA) to estimate the degree of technical, allocative and cost efficiency in individual public and private health centres in Zambia; and to identify the relative inefficiencies in the use of various inputs among individual health centers. About 83% of the 40 health centres were technically inefficient; and 88% of them were both allocatively and cost inefficient. The privately owned health centers were found to be more efficient than public facilities.[PUBLICATION ABSTRACT]

Although crisp data are fundamentally indispensable for determining the profit Malmquist productivity index (MPI), the observed values in real-world problems are often imprecise or vague. These imprecise or vague data can be suitably characterized with fuzzy and interval methods. In this paper, we reformulate the conventional profit MPI problem as an imprecise data envelopment analysis (DEA) problem, and propose two novel methods for measuring the overall profit MPI when the inputs, outputs, and price vectors are fuzzy or vary in intervals. We develop a fuzzy version of the conventional MPI model by using a ranking method, and solve the model with a commercial off-the-shelf DEA software package. In addition, we define an interval for the overall profit MPI of each decision-making unit (DMU) and divide the DMUs into six groups according to the intervals obtained for their overall profit efficiency and MPIs. We also present two numerical examples to demonstrate the applicability of the two proposed models and exhibit the efficacy of the procedures and algorithms.

The existing assignment problems for assigning n jobs to n individuals are limited to the considerations of cost or profit measured as crisp. However, in many real applications, costs are not deterministic numbers. This paper develops a procedure based on Data Envelopment Analysis method to solve the assignment problems with fuzzy costs or fuzzy profits for each possible assignment. It aims to obtain the points with maximum membership values for the fuzzy parameters while maximizing the profit or minimizing the assignment cost. In this method, a discrete approach is presented to rank the fuzzy numbers first. Then, corresponding to each fuzzy number, we introduce a crisp number using the efficiency concept. A numerical example is used to illustrate the usefulness of this new method.

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA. (C) 2008 Elsevier Ltd. All rights reserved.

Performance evaluation in conventional data envelopment analysis (DEA) requires crisp numerical values. However, the observed values of the input and output data in real-world problems are often imprecise or vague. These imprecise and vague data can be represented by linguistic terms characterised by fuzzy numbers in DEA to reflect the decision-makers’ intuition and subjective judgements. This paper extends the conventional DEA models to a fuzzy framework by proposing a new fuzzy additive DEA model for evaluating the efficiency of a set of decision-making units (DMUs) with fuzzy inputs and outputs. The contribution of this paper is threefold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA, (2) we propose a new fuzzy additive DEA model derived from the α -level approach and (3) we demonstrate the practical aspects of our model with two numerical examples and show its comparability with five different fuzzy DEA methods in the literature.

Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to different CPS systems. In this paper, we propose a new meta-heuristic algorithm based on Multiverse Theory, named MVA, that can solve NP-hard optimization problems such as non-linear and multi-level programming problems as well as applied optimization problems for CPS systems. MVA algorithm inspires the creation of the next population to be very close to the solution of initial population, which mimics the nature of parallel worlds in multiverse theory. Additionally, MVA distributes the solutions in the feasible region similarly to the nature of big bangs. To illustrate the effectiveness of the proposed algorithm, a set of test problems is implemented and measured in terms of feasibility, efficiency of their solutions and the number of iterations taken in finding the optimum solution. Numerical results obtained from extensive simulations have shown that the proposed algorithm outperforms the state-of-the-art approaches while solving the optimization problems with large feasible regions.

This study introduces a kernel Bayesian approach to correct the bias of data envelopment analysis (DEA) efficiency estimates. This approach yields consistent estimates for convex sets. The prior distribution of this Bayesian method is “non-informative” in a relative sense as no distributional assumptions are made, like in theoretical Bayesian approaches, and the parameters of DEA efficiency distributions are not used to obtain bias-corrected estimates, as in alternative computational or hybrid Bayesian techniques for statistical inference to efficiencies. Specifically, various kernel distributions, such as Epanechnikov, Biweight, Triweight, and Gaussian, are tested for the prior distribution. In addition, we deploy least cross validation (LCV), rule of thumb (RoT), and least-squares cross validation (LSCV) as bandwidth selection methodologies for every kernel distribution function. Bias correction draws on the ratio of a posterior truncated normal distribution, with μ and σ the respective kernel values, and the above prior kernel distributions with LCV, RoT, and LSCV as bandwidth selection mechanisms. Using scaled samples of 30, 50, 80, and 100 units, the mean square error (MSE) and mean absolute error (MAE) of this Bayesian approach’s estimates are as low as 6.45 × 10–3 and 6.4 × 10–2, respectively. Based on real-world data, we show that the new Bayesian method performs better than extant computational bias-correction techniques for DEA efficiencies. At the same time, the MSE and MAE decrease gradually as the sample size increases.

Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production. In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming. The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. •The focus of this paper is to identify excessive use of energy and optimize energy consumption in grape production.•We measure the efficiency as a function of labor/machinery/chemicals/farmyard manure/diesel-fuel/electricity/water.•Data were obtained from 41 grape vineyards; we found substantial inefficiency between the grape producers.•The main reason for being an inefficient farmer is excess use of chemicals, diesel-fuel and water for irrigation.•The second stage analysis revealed that the more educated farmers are more energy efficient.

This paper clarifies the role of alternative optimal solutions in the clustering of multidimensional observations using data envelopment analysis (DEA). The paper shows that alternative optimal solutions corresponding to several units produce different groups with different sizes and different decision making units (DMUs) at each class. This implies that a specific DMU may be grouped into different clusters when the corresponding DEA model has multiple optimal solutions. (C) 2011 Elsevier B.V. All rights reserved.

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the efficiency of a set of decision making units Such as firms or public sector agencies, first introduced into the operational research and management science literature by Charnes, Cooper, and Rhodes (CCR) [Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European journal of Operational Research 2, 429-444]. The original DEA models were applicable only to technologies characterized by positive inputs/outputs. In subsequent literature there have been various approaches to enable DEA to deal with negative data. In this paper, we propose a semi-oriented radial measure, which permits the presence of variables which can take both negative and positive values. The model is applied to data on a notional effluent processing system to compare the results with those yielded by two alternative methods for dealing with negative data in DEA: The modified slacks-based model suggested by Sharp et al. [Sharp, J.A., Liu, W.B., Meng, W., 2006. A modified slacks-based measure model for data envelopment analysis with 'natural' negative outputs and inputs. journal of Operational Research Society 57 (11) 1-6] and the range directional model developed by Portela et al. [Portela, M.C.A.S., Thanassoulis, E., Simpson, G., 2004. A directional distance approach to deal with negative data in DEA: An application to bank branches. journal of Operational Research Society 55 (10) 1111-1121]. A further example explores the advantages of using the new model. (C) 2009 Elsevier B.V. All rights reserved.

Allocating the fixed cost among a set of users in a fair way is an important issue both in management and economic research. Recently, Du et al. (Eur J Oper Res 235(1): 206-214, 2014) proposed a novel approach for allocating the fixed cost based on the game cross-efficiency method by taking the game relations among users in efficiency evaluation. This paper proves that the novel approach of Du et al. (Eur J Oper Res 235(1): 206-214, 2014) is equivalent to the efficiency maximization approach of Li et al. (Omega 41(1): 55-60, 2013), and may exist multiple optimal cost allocation plans. Taking into account the game relations in the allocation process, this paper proposes a cooperative game approach, and uses the nucleolus as a solution to the proposed cooperative game. The proposed approach in this paper is illustrated with a dataset from the prior literature and a real dataset of a steel and iron enterprise in China.

Data Envelopment Analysis (DEA), provides an empirical estimation of the production frontier, based on an observed sample of decision making units (DMUs). Except for the single input-single output case, the asymptotic distribution of the DEA estimator can only be approximated through bootstrapping approaches. Therefore, bootstrapping techniques have been widely applied in the DEA literature to make statistical inference for the cases when the production process has a single-stage structure. However, in many cases, the transformation of inputs into outputs has an inner structure that needs to be considered. This paper examines the applicability of the subsampling bootstrap procedure in the approximation of the asymptotic distribution of the DEA estimator when the production process has a network structure, and in the presence of undesirable factors. Evidence on the performance of subsampling bootstrap is obtained through Monte Carlo experiments for the case of two-stage series structures, where overall and stage efficiency estimates are calculated using the additive decomposition approach. Results indicate great sensitivity both to the sample and subsample size, as well as to the data generating process. Subsampling methodology is then applied to construct confidence interval estimates for the overall and stage efficiency scores of railways in 22 European countries, where the railway transport process is decomposed into two stages and the railway noise pollution problem is considered as an undesirable output.

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models - and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers. (C) 2015 Elsevier B.V. All rights reserved.

The purpose of this study is to provide a comparative analysis of the efficiency of Islamic and conventional banks in Gulf Cooperation Council (GCC) countries. In this study, we explain inefficiencies obtained by introducing firm-specific as well as macroeconomic variables. Our findings indicate that during the eight years of study, conventional banks largely outperform Islamic banks with an average technical efficiency score of 81% compared to 95.57%. However, it is clear that since 2008, efficiency of conventional banks was in a downward trend while the efficiency of their Islamic counterparts was in an upward trend since 2009. This indicates that Islamic banks have succeeded to maintain a level of efficiency during the subprime crisis period. Finally, for the whole sample, the analysis demonstrates the strong link of macroeconomic indicators with efficiency for GCC banks. Surprisingly, we have not found any significant relationship in the case of Islamic banks.

Data envelopment analysis (DEA) is defined based on observed units and by finding the distance of each unit to the border of estimated production possibility set (PPS). The convexity is one of the underlying assumptions of the PPS. This paper shows some difficulties of using standard DEA models in the presence of input-ratios and/or output-ratios. The paper defines a new convexity assumption when data includes a ratio variable. Then it proposes a series of modified DEA models which are capable to rectify this problem. (C) 2007 Elsevier Inc. All rights reserved.

Over the last few years Data Envelopment Analysis (DEA) has been gaining increasing popularity as a tool for measuring efficiency and productivity of Decision Making Units (DMUs). Conventional DEA models assume non-negative inputs and outputs. However, in many real applications, some inputs and/or outputs can take negative values. Recently, Emrouznejad et al. [6] introduced a Semi-Oriented Radial Measure (SORM) for modelling DEA with negative data. This paper points out some issues in target setting with SORM models and introduces a modified SORM approach. An empirical study in bank sector demonstrates the applicability of the proposed model. (C) 2014 Elsevier Ltd. All rights reserved.

This paper presents a price discrimination model for a manufacturer who acts in two different markets. In order to have a fair price discrimination model and compare monopoly and competitive markets, it is assumed that there is no competitor in the first market (monopoly market) and there is a strong competitor in the other market (competitive market). The manufacturer's objective is to maximise the total benefit in both markets. The decision variables are selling price, lot size, marketing expenditure, customer service cost, flexibility and reliability of production process, set-up costs, and quality of products. The proposed model in this paper is a signomial geometric programming problem which is difficult to solve and find the globally optimal solution. So, this signomial model is converted to a posynomial geometric type and using an iterative method, the globally optimal solution is found. To illustrate the capability of the proposed model, a numerical example is solved and the sensitivity analysis is implemented under different conditions.

The integration between blockchain and artificial intelligence (AI) has gained a lot of attention in recent years, especially since such integration can improve security, efficiency, and productivity of applications in business environments characterised by volatility, uncertainty, complexity, and ambiguity. In particular, supply chain is one of the areas that have been shown to benefit tremendously from blockchain and AI, by enhancing information and process resilience, enabling faster and more cost-efficient delivery of products, and augmenting products' traceability, among others. This paper performs a state-of-the-art review of blockchain and AI in the field of supply chains. More specifically, we sought to answer the following three principal questions: Q1-What are the current studies on the integration of blockchain and AI in supply chain?, Q2-What are the current blockchain and AI use cases in supply chain?, and Q3-What are the potential research directions for future studies involving the integration of blockchain and AI? The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI.

Abstract Management-led productivity improvements are crucial for achieving sustainable development, and the Malmquist productivity index is known to be useful in relevant contexts. This study aims to extend such index by using non-parametric mathematical modeling of production processes. Specifically, and in the spirit of the existing index, we introduce the directional distance function to develop a new one applicable to the joint production of desirable and undesirable outputs. Furthermore, we decompose the new index into two constituent components to provide more intuitive explanations when revealing the root sources of productivity changes over time. Under the cost minimization assumption, the new index is applicable when producers implement resource allocation management, and the input–output quantities and the micro-level input prices are known. The index emphasizes that the allocative efficiency should be regarded as an important aspect of productivity assessment like the technical efficiency. As a practical benchmarking tool, it can offer valuable information and provide appropriate strategies for managerial decision-making. The index’s application and usefulness is demonstrated in the commercial bank sector in China.

Health care organizations must continuously improve their productivity to sustain long-term growth and profitability. Sustainable productivity performance is mostly assumed to be a natural outcome of successful health care management. Data envelopment analysis (DEA) is a popular mathematical programming method for comparing the inputs and outputs of a set of homogenous decision making units (DMUs) by evaluating their relative efficiency. The Malmquist productivity index (MPI) is widely used for productivity analysis by relying on constructing a best practice frontier and calculating the relative performance of a DMU for different time periods. The conventional DEA requires accurate and crisp data to calculate the MPI. However, the real-world data are often imprecise and vague. In this study, the authors propose a novel productivity measurement approach in fuzzy environments with MPI. An application of the proposed approach in health care is presented to demonstrate the simplicity and efficacy of the procedures and algorithms in a hospital efficiency study conducted for a State Office of Inspector General in the United States.

One of the most important effects that railways have on the environment is noise pollution, notably in Europe. The purpose of this study is to evaluate the environmental efficiency of railways in 22 European countries, considering two factors; a country's response in retrofitting their wagon fleet with more silent braking technology and the number of people affected by railway noise. The railway transport process efficiency is decomposed into assets and service efficiency. The additive decomposition network Data Envelopment Analysis (NDEA) approach is customised to account for intermediate and undesirable outputs. Results suggest that Estonia, Germany and Poland are overall environmentally efficient and that except for Finland, asset efficient countries are also service efficient; the inverse does not hold. Sensitivity analysis revealed that efficiency rankings are robust to alterations in the decomposition weight restrictions. This is the first study that uses DEA to incorporate the noise-pollution problem in railway efficiency measurement.

•Develop a fractal framework for virtual network optimisation and assessment;•Introduce the windows multiplicative DEA model in the presence of ratio data;•Show that devices on virtual networks have distinct fractal behaviour over time;•Prediction of a virtual setting with higher and stable TCP performance by long time;•The DEA results' dataset is available at URL: http://dx.doi.org/10.17632/776sjbz7z5.5. Recently, the prediction of the most efficient configuration of a vast set of devices used for mounting an optimised cloud computing services and virtual networks environments have attracted growing attention. This paper proposes a paradigm shift in modelling transmission control protocol (TCP) behaviour over time in virtual networks by using data envelopment analysis (DEA) models. Firstly, it proves that self-similarity with long-range dependency is presented differently in every network device. This study implements a novel fractal dimension concept on virtual networks for prediction, where this key index informs if the transport layer forwards services with smooth or jagged behaviour over time. Another substantial contribution is proving that virtual network devices have a distinct fractal memory, TCP bandwidth performance, and fractal dimension over time, presenting themselves as important factor for forecasting of spatiotemporal data. Thus, a continuous stepwise fractal performance evaluation framework methodology is developed as an expert system for virtual network assessment and performs a fractal analysis as a knowledge representation. In addition, due to the limitations of classical DEA models, the windows multiplicative data envelopment analysis (WMDEA) model is used to dynamically assess the fractal time series from virtual network hypervisors. For knowledge acquisition, 50 different virtual network hypervisors were appraised as decision-making units (DMU). Finally, this expert system also acts as a math hypervisor capable of determining the correct fractal pattern to follow when delivering TCP services in an optimised virtual network.

Bogetoft and Wang proposed admirable production economic models to estimate and decompose the potential gains from mergers. They provided a good platform to quantify the merger efficiency and related it to relevant organisational changes ex-ante. In this paper, we develop an alternative approach to decompose the potential overall gains from mergers into to technical effect, size effect, and harmony effect. The proposed approach uses strongly efficient projections, and consistently calculates radial input-based measures for these three effects based on the pre-merger aggregated inputs. In addition, the proposed approach is of vital significance in two special cases where the aggregated projected inputs are not proportional to the pre-merger aggregated inputs and where the production sizes are very different for the original decision-making units. Finally, an application to the City Commercial Banks (CCBs) in China is provided to illustrate the usefulness and efficacy of the proposed approach. The application shows that there exist significant merger efficiency gains for these top 20 CCBs. Further, both the technical effect and harmony effect favour mergers, whereas the size effect would work against most mergers. Thus, in most cases the full-size merger with "organisational sense" is not proper.

Contemporary research in various disciplines from social science to computer science, mathematics and physics, is characterized by the availability of large amounts of data. These large amounts of data present various challenges, one of the most intriguing of which deals with knowledge discovery and large-scale data-mining. This chapter investigates the research areas that are the most influenced by big data availability, and on which aspects of large data handling different scientific communities are working. We employ scientometric mapping techniques to identify who works on what in the area of big data and large scale optimization problems.

This paper aims to address the problem of allocating the CO 2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO 2 emission reduction is conducted in a three-stage phases. The first stage is to obtain the total amount CO 2 emission reduction from the Chinese government goal as our total CO 2 emission quota to reduce. The second stage is to allocate the reduction quota to different two-digit level manufacturing industries in China. The third stage is to further allocate the reduction quota for each industry into different provinces. A new inverse data envelopment analysis (InvDEA) model is developed to achieve our goal to allocate CO 2 emission quota under several assumptions. At last, we obtain the empirical results based on the real data from Chinese manufacturing industries.

Data Envelopment Analysis (DEA) is a powerful method for analyzing the performance of decision making units (DMUs). Traditionally, DEA is applied for estimating the performance of a set of DMUs through measuring a single perspective of efficiency. However, in recent years, due to increasing competition in various industries, modern enterprises focus on enhancing their performance by measuring efficiencies in different aspects, separately or simultaneously. This paper proposes a bi-level multi-objective DEA (BLMO DEA) model which is able to assess the performance of DMUs in two different hierarchical dimensions, simultaneously. In the proposed model, we define two level efficiency scores for each DMU. The aim is to maximize these two efficiencies, simultaneously, for each DMU. Since the objective functions at both levels are fractional, a fuzzy fractional goal programming (FGP) methodology is used to solve the proposed BLMO DEA model. The capability of the proposed model is illustrated by a numerical example. Finally, to practically validate the proposed model, a real case study from 45 bank’s branches is applied. The results show that the proposed model can provide a more comprehensive measure for efficiency of each bank’s branch based on simultaneous measuring of two different efficiencies, profit and operational efficiencies, and by considering the level of their importance.

This paper proposes a new framework for evaluating the performance of employment offices based on non-parametric technique of data envelopment analysis. This framework is explained using the assessment of technical efficiency of 82 employment offices in Tunisia which are under the direction of the National Agency for Employment and Independent Work. We further investigated the exogenous factors that may explain part of the variation in efficiency scores using a bootstrapping approach in period January 2006 to December 2008. Given the specialisation of employment offices, we used the proposed approach for the efficiency evaluation of graduate employment offices and multi-services employment offices, separately.

Due to the urbanization and economic growth, planning of regional sustainable development has become one of the major challenges in the world. The key indicators such as gross domestic product (GDP), electricity and energy consumption and greenhouse gas emission (GHG) are considered in sustainable development planning. This paper determines number of required workforce in different sectors of each province in Iran considering targets/goals for sustainable development indicators in the 2030 macroeconomic and regional planning. First, the relative goals are designed for GDP, electricity, energy and GHG emission and then, two weighted goal programming models are applied to allocate the optimal workforce among four sectors: agriculture, industry, services and transportation. The first model minimizes recruitment of new workforce and allows current workforce exchange among the four sectors in each province in order to achieve the goals, while the second model indicates equitable distribution of new workforce recruitment in different sectors within each province. In both models, the workforce changes have been investigated based on achieving the desirable growth rates of GDP, GHG, electricity and energy consumption as planned by the government. Based on the results of this paper, policy makers can manage workforce and the government can make optimized decisions to macroeconomic and regional planning.

Measuring variations in efficiency and its extension, eco-efficiency, during a restructuring period in different industries has always been a point of interest for regulators and policy makers. This paper assesses the impacts of restructuring of procurement in the Iranian power industry on the performance of power plants. We introduce a new slacks-based model for Malmquist-Luenberger (ML) Index measurement and apply it to the power plants to calculate the efficiency, eco-efficiency, and technological changes over the 8-year period (2003-2010) of restructuring in the power industry. The results reveal that although the restructuring had different effects on the individual power plants, the overall growth in the eco-efficiency of the sector was mainly due to advances in pure technology. We also assess the correlation between efficiency and eco-efficiency of the power plants, which indicates a close relationship between these two steps, thus lending support to the incorporation of environmental factors in efficiency analysis. (C) 2014 Elsevier Ltd. All rights reserved.

With increasing growth of electricity consumption in developed and developing countries, the necessity of constructing and developing of power plants is inevitable. There are two main resources for electricity generation includes fossil and renewable energies which have some different characteristics such as manufacturing technology, environmental issues, accessibility and etc. In developing plans, it is important to consider and address the policy makers' indicators such as environmental, social, economic and technical criteria. In this paper, an integrated multi response Taguchi-neural network-fuzzy best-worst method (FBWM) -TOPSIS approach is applied to find an optimal level of five different power plants including: gas, steam, combined cycle, wind and hydroelectric. Taguchi method is used to design combinations and calculate some of the signal to noise (S/N) ratios. Then, neural network is applied to estimate the rest of S/N ratios. Finally, FBWM and TOPSIS methods are used for weighing sub-indicators and selecting the best combination, respectively. To illustrate the usefulness of the proposed approach, a case study on the development of power plants in Iran is considered and the results are discussed. According to the results, in general, small size power plants for fossil resources are preferable. In contrast, medium and larger size power plants for renewable resources are preferable. (C) 2018 Elsevier Ltd. All rights reserved.

This paper explores the use of the optimization procedures in SAS/OR software with application to the contemporary logistics distribution network design using an integrated multiple criteria decision making approach. Unlike the traditional optimization techniques, the proposed approach, combining analytic hierarchy process (AHP) and goal programming (GP), considers both quantitative and qualitative factors. In the integrated approach, AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, a GP model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. To facilitate the use of integrated multiple criteria decision making approach by SAS users, an ORMCDM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear programming models based on the selected GP model. An example is given to illustrate how one could use the code to design the logistics distribution network. (C) 2008 Elsevier Ltd. All rights reserved.

Maize is the main staple food for most Kenyan households, and it predominates where smallholder, as well as large-scale, farming takes place. In the sugarcane growing areas of Western Kenya, there is pressure on farmers on whether to grow food crops, or grow sugarcane, which is the main cash crop. Further, with small and diminishing land sizes, the question of productivity and efficiency, both for cash and food crops is of great importance. This paper, therefore, uses a two-step estimation technique (DEA meta-frontier and Tobit Regression) to highlight the inefficiencies in maize cultivation, and their causes in Western Kenya.

Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res. 180 (2007) 692-699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach.

The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

In this paper, we propose a new similarity measure to compute the pair-wise similarity of text-based documents based on patterns of the words in the documents. First we develop a kappa measure for pair-wise comparison of documents then we use ordered weighting averaging operator to define a document similarity measure for a set of documents.

The Chinese government announced to cut its carbon emissions intensity by 60%-65% from its 2005 level. To realize the national abatement commitment, a rational allocation into its subunits (i.e. industries, provinces) is eagerly needed. Centralized allocation models can maximize the overall interests, but might cause implementation difficulty and fierce resistance from individual subunits. Based on this observation, this article will address the carbon emission abatement quota allocation problem from decentralized perspective, taking the competitive and cooperative relationships simultaneously into account. To this end, this article develops an integrated cooperative game data envelopment analysis (DEA) approach. We first investigate the relative efficiency evaluation by taking flexible carbon emission abatement allocation plans into account, and then define a super-additive characteristic function for developing a cooperative game among units. To calculate the nucleolus-based allocation plan, a practical computation procedure is developed based on the constraint generation mechanism. Further, we present a two-layer way to allocate the CO2 abatement quota into different sub-industries and further different provinces in Chinese manufacturing industries. The empirical results show that five sub-industries (Processing of petroleum, coking and processing of nuclear fuel; Smelting and pressing of ferrous metals; Manufacture of non-metallic mineral products; Manufacture of raw chemical materials and chemical product; Smelting and pressing of non-ferrous metals) and two provinces (Guangdong and Shandong) will be allocated more than 10% of the total national carbon emission abatement quota.

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by I he 'COOPER-framework' a comprehensive model for carrying out non-parametric projects. The framework consists of six interrelated phases: Concepts and objectives, On structuring data, Operational models, Performance comparison model, Evaluation, and Result and deployment. Each of the phases describes some necessary steps a researcher should examine for a well defined and repeatable analysis. The COOPER-framework provides for the novice analyst guidance, structure and advice for a sound non-parametric analysis. The more experienced analyst benefits from a check list such that important issues are not forgotten. In addition, by the use of a standardized framework non-parametric assessments will be more reliable, more repeatable, more manageable, faster and less costly. (C) 2010 Elsevier B.V. All rights reserved.

By introducing the concept of sustainable development, managers and policymakers in many industries have been encouraged to consider environmental and social issues in addition to economic objectives in their planning. Following this concept, sustainable supply chain management has become the main concern of many studies. Among all the strategies to achieve sustainability targets in a supply chain, cooperating with third-party logistics companies has attracted lots of attention. By providing more sustainable and efficient transportation services, 3PLs can help all types of regular, closed-loop, and circular SCs achieve more profit, while they are still sustainable, at least in distribution and collection/recycling stages. This study investigates the sustainable multi-channel SC design problem in the presence of the government and 3PLs. To bring the present study closer to the real-world situation, the problem is modeled using an intuitionistic fuzzy uncertainty approach. Considering the government as the leader of the SC in two centralized and decentralized decision structures, game theory has been applied to model the game between players and obtain optimal decision values. For the first time in the literature, public awareness toward green activities of the players, emission reduction, uncertainty, and delivery time have been considered in this study. The results show the presence of a 3PL will reduce the delivery time and the amount of pollution. Also, the findings confirm that governments can control the players' activities and encourage them to apply green strategies using financial tools.

Purpose The purpose of this paper is to measure the technical and scale efficiency of health centres to evaluate changes in productivity and to highlight possible policy implications of the results for policy makers. Designmethodologyapproach Data envelopment analysis DEA is employed to assess the technical and scale efficiency, and productivity change over a fouryear period among 17 public health centres. Findings During the period of study, the results suggest that the public health centres in Seychelles have exhibited mean overall or technical efficiency of above 93 per cent. It was also found that the overall productivity increased by 2.4 per cent over 20012004. Research limitationsimplications Further research can be undertaken to gather data on the prices of the various inputs to facilitate an estimation of the allocative efficiency of clinics. If such an exercise were to be undertaken, researchers may also consider collecting data on quantities and prices of paramedical, administrative and support staff to ensure that the analysis is more comprehensive than the study reported in this paper. Institutionalization of efficiency monitoring would help to enhance further the already good health sector stewardship and governance. Originalityvalue This paper provides new empirical evidence on a fouryear trend in the efficiency and productivity of health centres in Seychelles.

Green technology innovation is integral to fostering sustainable economic development and environmental conservation. The application of smart product platform has emerged as a transformative force in shaping the trajectory of green technology innovation. Drawing on the foundation of knowledge theory and utilizing a sample of 1240 valid cases from listed Chinese automotive manufacturing enterprises between 2013 and 2022, this paper employs a mixed regression method to explore the relationship between smart product platform usage and green technology innovation. Additionally, it analyzes the mediating role of corporate innovation input in this relationship and the moderating effect of intelligentization level on the association between smart product platform usage and green technology innovation. The research reveals the following key findings: (1) Corporate smart product platform usage and innovation input significantly and positively drive green technology innovation. (2) Innovation input acts as a complete mediator in the relationship between smart product platform usage and green technology innovation. (3) Corporate intelligentization level positively moderates the relationship between smart product platform usage and innovation input. (4) Considering regional and ownership attribute disparities, the impact of smart product platform usage on green technology innovation is asymmetric. This study, as one of the few empirical examinations of the effects of smart product platform, enriches research in the areas of smart product platform and green technology innovation. The study, contributing to the empirical understanding of smart product platforms, offers valuable insights for automotive manufacturing enterprises to strategically leverage these platforms for advanced and flexible operations, aligning with the imperative of achieving global environmental goals such as NetZero. As indicated in COP28, the findings underscore the urgency of integrating smart technologies into the automotive industry to advance these crucial environmental objectives

In many real applications of Data Envelopment Analysis (DEA), the decision makers have to deteriorate some inputs and some outputs. This could be because of limitation of funds available. This paper proposes a new DEA-based approach to determine highest possible reduction in the concern input variables and lowest possible deterioration in the concern output variables without reducing the efficiency in any DMU. A numerical example is used to illustrate the problem. An application in banking sector with limitation of IT investment shows the usefulness of the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.

Efficiency in the mutual fund (MF), is one of the issues that has attracted many investors in countries with advanced financial market for many years. Due to the need for frequent study of MF’s efficiency in short-term periods, investors need a method that not only has high accuracy, but also high speed. Data envelopment analysis (DEA) is proven to be one of the most widely used methods in the measurement of the efficiency and productivity of decision making units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper uses neural network back-propagation DEA in measurement of mutual funds efficiency and shows the requirements, in the proposed method, for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of a large set of MFs.

For a specific query merging the returned results from multiple search engines, in the form of a metasearch aggregation, can provide significant improvement in the quality of relevant documents. This paper suggests a minimax linear programming (LP) formulation for fusion of multiple search engines results. The paper proposes a weighting method to include the importance weights of the underlying search engines. This is a two-phase approach which in the first phase a new method for computing the importance weights of the search engines is introduced and in the second stage a minimax LP model for finding relevant search engines results is formulated. To evaluate the retrieval effectiveness of the suggested method, the 50 queries of the 2002 TREC Web track were utilized and submitted to three popular Web search engines called Ask, Bing and Google. The returned results were aggregated using two exiting approaches, three high-performance commercial Web metasearch engines and our proposed technique. The efficiency of the generated lists was measured using TREC-Style Average Precision (TSAP). The new findings demonstrate that the suggested model improved the quality of merging considerably.

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

To compare the accuracy of different forecasting approaches an error measure is required. Many error measures have been proposed in the literature, however in practice there are some situations where different measures yield different decisions on forecasting approach selection and there is no agreement on which approach should be used. Generally forecasting measures represent ratios or percentages providing an overall image of how well fitted the forecasting technique is to the observations. This paper proposes a multiplicative Data Envelopment Analysis (DEA) model in order to rank several forecasting techniques. We demonstrate the proposed model by applying it to the set of yearly time series of the M3 competition. The usefulness of the proposed approach has been tested using the M3-competition where five error measures have been applied in and aggregated to a single DEA score.

In the last two decades there have been substantial developments in the mathematical theory of inverse optimization problems, and their applications have expanded greatly. In parallel, time series analysis and forecasting have become increasingly important in various fields of research such as data mining, economics, business, engineering, medicine, politics, and many others. Despite the large uses of linear programming in forecasting models there is no a single application of inverse optimization reported in the forecasting literature when the time series data is available. Thus the goal of this paper is to introduce inverse optimization into forecasting field, and to provide a streamlined approach to time series analysis and forecasting using inverse linear programming. An application has been used to demonstrate the use of inverse forecasting developed in this study.

Modern Turnip production methods need significant amount of direct and indirect energy. The optimum use of agricultural input resources results in the increase of efficiency and the decrease of the carbon footprint of turnip production. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to evaluate the efficiency for peer units compared with the best practice frontier, widely used by researches to analyze the performance of agricultural sector. In this regard, a new non-radial DEA-based efficiency model is designed to investigate the efficiency of turnip farms. For this purpose, five inputs and two outputs are considered. The outputs consist turnip yield as a desirable output and greenhouse gas emission as an undesirable output. The new model projects each DMU on the strong efficient frontier. Several important properties are stated and proved which show the capabilities of our proposed model. The new models are applied in evaluating 30 turnip farms in Fars, Iran. This case study demonstrates the efficiency of our proposed models. The target inputs and outputs for these farms are also calculated and the benchmark farm for each DMU is determined. Finally, the reduction of CO2 emission for each turnip farm is evaluated. Compared with other factors like human labor, diesel fuel, seed and fertilizers, one of the most important findings is that machinery has the highest contribution to the total target energy saving. Besides, the average target emission of turnip production in the region is 7% less than the current emission. (C) 2018 Elsevier Ltd. All rights reserved.

The productivity of the banks in any country is a key factor in the growth and development of that country's economy. Recently, the evaluation and improvement of the productivity of the banking industry has been taken into much consideration in Iran. Data Envelopment Analysis (DEA) is a comprehensive and accepted approach for assessing the performance of banking industry. Although extensive studies have been done on banking industry using standard DEA models, they are, in fact, they ignore the internal structure of bank performance. Since the overall operational process of the banking system is made up of several partial processes, network DEA models are used to take into account all the internal components of the process and the coherence of the whole process. This is also done as the evaluation of the efficiency of partial processes helps to identify the sources of inefficiency of the overall banking system. In the present study, a network Slacks-Based Measure (SBM) DEA model is used in which the efficiency of the overall system is equal to the weighted average of the efficiency of the individual stages. The main advantage of this model is its ability to provide better efficiency criteria, calculate the weight of each stages separately, and simultaneously evaluate the mediator variables as both input and output. Finally, the comprehensive performance evaluation of banking industry is designed in three divisions, namely, production, intermediation, and social welfare approach. The model is applied to simultaneously evaluate operational efficiency, service effectiveness, and social effectiveness for 37 branches of one of the largest commercial banks in Iran.

The field of production economics has rapidly changed over the last decades. This rapid change is partly due to the Data Envelopment Analysis (DEA) technique, which assesses the comparative performance of a set of units based on inputs and outputs, measuring the efficiency of the transformation procedure. The inputs are consumed in order to produce outputs; thus, the fraction of outputs produced to inputs consumed is the efficiency of the transformation.

The emissions trading system allows organizations to transact emission permits to fit their production practice. This paper develops a new nonparametric methodology for performance evaluation of organizations (or decision-making units, DMUs) considering carbon emission permit trading. Explicit production axioms are discussed, and a new production technology considering carbon emission permit trading is proposed. Models based on the new production technology are established for evaluating the carbon emission reduction potential and performance of the DMUs. Comparing the proposed models with previous ones, the adoption of carbon emission permit trading increases the potentials of DMUs to reduce carbon dioxide emission and improve inputs and outputs. In addition, a proper increase of the carbon emission permit trading price can increase the potential of DMUs to reduce carbon dioxide emissions. The proposed approach contributes to the literature by explicitly explaining how adopting carbon emission permit trading affects production technology. A numeral example illustrates the proposed approach while the usefulness and practicality of the models are explained by applying them to China's thermal power industry. •Emission trading mechanism is investigated for organization performance evaluation.•New production technology considering carbon emission trading is built.•Models are proposed to estimate carbon emission potential and evaluate efficiency.•Adoption effects of carbon emission permit trading is explicitly explained.•China's thermal industry is investigated.

Data Envelopment Analysis models have evolved over the years, offering formulations that correspond to instances beyond strictly measuring performance. In this chapter, extensions of DEA models will be presented, demonstrating recent developments in DEA formulations. The models that will be analytically described and modelled with GAMS in this chapter concern DEA models with exogenously fixed variables and categorical variables, DEA models for handling desirable and undesirable outputs, congestion, and chance constraints.

One of the most widely used software for mathematical programming is the General Algebraic Modeling System (GAMS). The software is user-friendly and facilitates the process of going from a mathematical statement of the problem to its solution. The main use of GAMS is for optimisation. One of the features that makes the GAMS software easy to use and popular in academic and commercial settings is that the user can provide the mathematical model while GAMS transforms it into representations required by solvers (CPLEX, BARON, and so on).

When input prices are available, cost efficiency, also known as input overall efficiency, or input allocative efficiency (as proposed by Färe et al., 1985) can be estimated. Alternatively, when output prices are available, revenue efficiency, also known as output overall efficiency, or output allocative efficiency (as proposed by Färe et al., 1985) can be estimated. Lastly, when both input and output prices are available, profit efficiency or profit allocative efficiency (as proposed by Chambers et al., 1998) can be estimated.

In May 2006, the Ministers of Health of all the countries on the African continent, at a special session of the African Union, undertook to institutionalise efficiency monitoring within their respective national health information management systems. The specific objectives of this study were: (i) to assess the technical efficiency of National Health Systems (NHSs) of African countries for measuring male and female life expectancies, and (ii) to assess changes in health productivity over time with a view to analysing changes in efficiency and changes in technology. The analysis was based on a five-year panel data (1999-2003) from all the 53 countries of continental Africa. Data Envelopment Analysis (DEA) - a non-parametric linear programming approach - was employed to assess the technical efficiency. Malmquist Total Factor Productivity (MTFP) was used to analyse efficiency and productivity change over time among the 53 countries' national health systems. The data consisted of two outputs (male and female life expectancies) and two inputs (per capital total health expenditure and adult literacy). The DEA revealed that 49 (92.5%) countries' NHSs were run inefficiently in 1999 and 2000, 50 (94.3%), 48 (90.6%) and 47 (88.7%) operated inefficiently in 2001, 2002, and 2003 respectively. All the 53 countries' national health systems registered improvements in total factor productivity attributable mainly to technical progress. Fifty-two countries did not experience any change in scale efficiency, while thirty (56.6%) countries' national health systems had a Pure Efficiency Change (PEFFCH) index of less than one, signifying that those countries' NHSs pure efficiency contributed negatively to productivity change. All the 53 countries' national health systems registered improvements in total factor productivity, attributable mainly to technical progress. Over half of the countries' national health systems had a pure efficiency index of less than one, signifying that those countries' NHSs pure efficiency contributed negatively to productivity change. African countries may need to critically evaluate the utility of institutionalising Malmquist TFP type of analyses to monitor changes in health systems economic efficiency and productivity over time. Adapted from the source document.

Evaluating the efficiency of electricity distribution companies (EDCs) accurately is one of the most important issues for regulators and policy makers. This research combines the results of data envelopment analysis (DEA) and corrected ordinary least squares (COLS) with machine learning techniques to evaluate a set of EDCs in the period 2011–2020. We propose a three-stage process. First, for each year, the efficiency scores of EDCs are measured using DEA and COLS methods. Then, this study applies support vector regression (SVR), a powerful machine learning technique, to estimate the efficient frontier and to calculate the efficiency of the EDCs. The efficiencies generated by DEA, COLS, and SVR are not the same and are used to construct fuzzy triangular numbers. Finally, the fuzzy efficiencies are considered as criteria for the technique for order performance by similarity to the ideal solution (TOPSIS), and the final efficiencies and ranks are obtained using the fuzzy TOPSIS (FTOPSIS) method. In addition, using the fuzzy C-means clustering (FCM) algorithm, the EDCs are clustered and discussed. The results show that there are increasing and decreasing trends for the selected EDCs in the period 2011–2022. In addition, some EDCs act in a poor situation and their performance should be improved. •Introducing a comprehensive approach to measure efficiency of EDCs.•Measuring efficiency of EDCs using DEA, COLS and SVR methods.•Combining the results of DEA, COLS and SVR by fuzzy TOPSIS method.•Clustering EDCs based on the scores generated by all methods.

The 2nd International Conference on Computer Science and Application Engineering (CSAE 2018) was successfully held in Hohhot during October 22-24, 2018. The conference was to provide a high forum for researchers, scholars, and engineers in the general areas of Computer Sciences and Application Engineering to disseminate their latest research results and exchange views on the future research directions of these fields, to exchange computer science and integrate of its practice, application of the academic ideas, improve the academic depth of computer science and its application, provide an international communication platform for technology and scientific research for the world universities, business intelligence engineering field experts, professionals, and business executives.

This book contains the proceedings of the 9th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2024). This year, COMPLEXIS was held in Angers, France, from April 28 - 29, 2024. It was sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). COMPLEXIS 2024 was also organized in cooperation with the Centre of Complex Systems IPN and the Centre for Complex Systems Studies. The International Conference on Complexity, Future Information Systems and Risk, is a yearly meeting place for presenting and discussing innovative views on all aspects of Complex Information Systems, in different areas such as Informatics, Telecommunications, Computational Intelligence, Biology, Biomedical Engineering and Social Sciences. Information is pervasive in many areas of human activity – perhaps all – and complexity is a characteristic of current Exabyte-sized, highly connected and hyper dimensional, information systems. COMPLEXIS 2024 received 19 paper submissions from 14 countries of which 32% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least 2 but usually 3 or more members of the International Program Committee, which is composed of established researchers and domain experts. The high quality of the COMPLEXIS 2024 program is enhanced by the keynote lecture delivered by distinguished speakers who are renowned experts in their fields: Luigi Atzori (Università degli Studi di Cagliari, Italy) and Samuel Fosso Wamba (Toulouse Business School, France). All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index. As recognition for the best contributions, several awards based on the combined marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as assessed by session chairs at the conference venue, are conferred at the closing session of the conference. A shortlist of papers presented at the conference will be selected for recommended of extended and revised versions in the special issues of the Springer Nature Computer Science Journal, Journal of Global Information Management, IMA Journal of Management Mathematics, Socio-Economic Planning Sciences and Big Data Journal, Big Data Journal and Internet of Things . The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are herewith recorded. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly capable team. We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of COMPLEXIS, details of which are available at https://complexis.scitevents.org

We are delighted to welcome you all to DEA40: International Conference on Data EnvelopmentAnalysis, at Aston Business School, Aston University. This conference has been organised tocelebrate the success of DEA on the 40th Anniversary of the publication of the seminal paperon DEA by Charnes, Cooper and Rhodes in the European Journal of Operational Research. Thepaper has spawned a multitude of papers on the theory and application of DEA taking forwardefficiency and productivity analysis in a variety of directions.Aston Business School is one of the largest and most successful Business Schools in Europe. Itis among the elite 1% of Business Schools worldwide who have achieved triple accreditationfrom AMBA, EQUIS and AACSB, the standard accrediting bodies of the UK, Europe and theUSA. The School is within Aston University, located in the centre of the second largest city ofEngland, Birmingham.Birmingham, with a population of one million inhabitants, is a vibrant multi-ethnic city and thecentre of a wider region of some 7 million people. Long since known as the home ofmanufacturing and of the automotive industry in the UK, the city has in recent times addededucation, finance, transport and many other services to its booming economy. While in the cityyou will have the opportunity to sample both its historical and its modern attractions, many ofwhich are within walking distance of the conference venue.We are very pleased about both the number and the quality of the papers submitted. Delegateswill have the choice between five parallel streams and several plenaries / semi-plenaries. Asconference organisers, we are especially happy to support those new to research or to researchin the efficiency and productivity area. The Conference has been designed with special attentionto such researchers and features not only a Special Issue of the Annals of Operations Researchbut also sessions with special advice for conducting and publishing research in this area. Wehave also been keen to embrace the users of our research and to that end we have organisedsemi-plenaries on applications of DEA in Banking, Finance and Education and a semi-plenaryand a plenary on the use of DEA in Regulation.We hope that this selection of articles presented in the DEA40 would be useful, and it shouldbe noted that authors are responsible for the content of their papers (9) (PDF) Data Envelopment Analysis and Performance Measurement: Recent Developments. Available from: https://www.researchgate.net/publication/328853250_Data_Envelopment_Analysis_and_Performance_Measurement_Recent_Developments [accessed Jun 20 2024].

In this book, we describe how the General Algebraic Modelling System (GAMS), a computationally efficient tool to deal with complex optimisation problems, can be used to solve a range of DEA models. The book is the first of its kind to provide readers with a comprehensive reference that includes the solution codes for a range of DEA models in GAMS.

Although many papers describe the evolution of the analytic hierarchy process (AHP), most adopt a subjective approach. This paper examines the pattern of development of the AHP research field using social network analysis and scientometrics, and identifies its intellectual structure. The objectives are: (i) to trace the pattern of development of AHP research; (ii) to identify the patterns of collaboration among authors; (iii) to identify the most important papers underpinning the development of AHP; and (iv) to discover recent areas of interest. We analyse two types of networks: social networks, that is, co-authorship networks, and cognitive mapping or the network of disciplines affected by AHP. Our analyses are based on 8441 papers published between 1979 and 2017, retrieved from the ISI Web of Science database. To provide a longitudinal perspective on the pattern of evolution of AHP, we analyse these two types of networks during the three periods 1979-1990, 1991-2001 and 2002-2017. We provide some basic statistics on AHP journals and researchers, review the main topics and applications of integrated AHPs and provide direction for future research by highlighting some open questions.

We highlight the state-of-the-art in the eco-efficiency measurement using Data Envelopment Analysis, including Malmquist-Luenberger productivity index. We also consider productivity change over time, provide directions for future studies in the field, and gather the most recent policy suggestions for governments, organisations and sectors for reducing CO2 emissions. A structured literature search of the Web of Science academic database reveals 311 papers published between 1989 and 2022. We carry out network analysis of citations to show the evolution of the literature in this research topic. In doing so, we (a) examine the key-route main path of knowledge flows, (b) provide basic bibliometric information about the most active journals and authors, (c) conduct a qualitative in-depth analysis of the identified most important studies and (d) identify the research fronts and relate them to the emerging issues on the topic researched, focusing on the most recent period between 2000 and 2022. Based on the insights of the literature review, the second part of this paper critically analyses the papers on the key-route (main path) of this subject. This review can be used as guidance and a starting point for researchers and practitioners that want to further investigate optimal policies to reach NetZero.

The inverse data envelopment analysis (DEA) is an advanced complementary method for efficiency analysis using the classical DEA approach. One of the inverse DEA (InvDEA) method applications is the mergers and acquisitions problem. It can be used for analyzing any under evaluation mergers and acquisitions. The current article is the first attempt to propose a novel inverse structure for the DEA model using the multiplier forms. This eventuates the possibility of incorporating decision maker preferences within the merger analysis. Moreover, compared with the existing models in the literature, the proposed novel models are capable of analyzing multiple merger scenarios simultaneously in a single method based on the common set of weights (CSW) rather than a series of models for studying multiple scenarios of mergers and acquisitions. Practically, this property enables decision-makers to consider and analyze multiple mergers and find possible potentials at the same time. The applicability of the proposed model is investigated by using a real-world dataset in banking.

In response to the limitation of classical Data Envelopment Analysis (DEA) models, the super efficiency DEA models, including Andersen and Petersen (Manag Sci 39(10): 1261-1264, 1993)'s model (hereafter called AP model) and Li et al. (Eur J Oper Res 255(3): 884-892, 2016)'s cooperative-game-based model (hereafter called L-L model), have been proposed to rank efficient decision-making units (DMUs). Although both models have been widely applied in practice, there is a paucity of research examining the performance of the two models in ranking efficient DMUs. Consequently, it is unclear how close the rankings obtained by the two models are to the "true" ones. Among the very few studies, Banker et al. (Ann Oper Res 250(1): 21-35, 2017) pointed out that the ranking performance of the AP model is unsatisfactory; Li et al. (Eur J Oper Res 255(3): 884-892, 2016) and Hinojosa et al. (Exp Syst Appl 80(9): 273-283, 2017) demonstrated the L-L model's capability of ranking efficient DMUs without addressing the ranking performance. In this study, we, thus, examine the ranking performance of the two super-efficiency models. In evaluating their performance, we carry out Monte Carlo simulations based on the well-known Cobb-Douglas production function and adopt Kendall rank correlation coefficient. Unlike Banker et al. (Ann Oper Res 250(1): 21-35, 2017), we use the rankings obtained based on the two models and the "true" ones as the basis of performance evaluation in our simulations. Moreover, we consider several types of returns to scale (RS) and study the impact of changes of some parameters on the ranking performance. In view of the importance, we also carry out additional simulations to examine the influence of technical inefficiency on the two models' ranking performance. Based on the simulation results, we conclude: (1) Under different RS, the ranking performance of the two models remains the same when changing parameters, e.g., the distribution of input variables; (2) Under different RS, when technical inefficiency (in comparison with random noise) is more important, the two models have satisfactory performance by providing rankings that are close to, or the same as, the "true" ones; (3) The L-L model has better performance than the AP model and is more robust. This is especially true when technical inefficiency is less important; (4) Under different RS, when technical inefficiency is less important, both models have unsatisfactory ranking performance; and (5) The relative importance of technical inefficiency plays an prominent role in ranking efficient DMUs.

With the increasing global demands for energy, fuel supply management is a challenging task of today's industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations.

This paper contributes to extend the minimax disparity to determine the ordered weighted averaging (OWA) model based on linear programming. It introduces the minimax disparity approach between any distinct pairs of the weights and uses the duality of linear programming to prove the feasibility of the extended OWA operator weights model. The paper finishes with an open problem.

This paper introduces a compact form for the maximum value of the non-Archimedean in Data Envelopment Analysis (DEA) models applied for the technology selection, without the need to solve a linear programming (LP). Using this method the computational performance the common weight multi-criteria decision-making (MCDM) DEA model proposed by Karsak and Ahiska (International Journal of Production Research, 2005, 43(8), 1537-1554) is improved. This improvement is significant when computational issues and complexity analysis are a concern.

Due to the urban expansion and population increasing, bus network design is an important problem in the public transportation. Functional aspect of bus networks such as the fuel consumption and depreciation of buses and also spatial aspects of bus networks such as station and terminal locations or access rate to the buses are not proper conditions in most cities. Therefore, having an efficient method to evaluate the performance of bus lines by considering both functional and spatial aspects is essential. In this paper, we propose a new model for the bus terminal location problem using data envelopment analysis with multi-objective programming approach. In this model, we want to find efficient allocation patterns for assigning stations terminals, and also we investigate the optimal locations for deploying terminals. Hence, we use a genetic algorithm for solving our model. By using the simultaneous combination of data envelopment analysis and bus terminal location problem, two types of efficiencies are optimized: Spatial efficiency as measured by finding allocation patterns with the most serving amount and the terminals' efficiency in serving demands as measured by the data envelopment analysis efficiency score for selected allocation patterns. This approach is useful when terminals' efficiency is one of the important criteria in choosing the optimal terminals location for decision-makers.

Data envelopment analysis (DEA) has been widely applied in measuring the efficiency of homogeneous decision-making units. Network DEA, as an important branch of DEA, was built to examine the internal structure of a system, whereas traditional DEA models regard a system as a 'black box'. However, only a few previous studies on parallel systems have considered the interdependent relationship between system components. In recent years, parallel interdependent processes systems commonly exist in production systems because of serious competition among organisations. Thus, an approach to measure the efficiency of such systems should be proposed. This paper builds an additive DEA model to measure a parallel interdependent processes system with two components which have an interdependent relationship. Then, the model is applied to analyse the '985 Project' universities in China, and certain policy implications are explained.

We developed an alternative approach for measuring information and communication technology (ICT), applying Data Envelopment Analysis (DEA) using data from the International Telecommunications Union as a sample of 183 economies. We compared the ICT-Opportunity Index (ICT-OI) with our DEA-Opportunity Index (DEA-OI) and found a high correlation between the two. Our findings suggest that both indices are consistent in their measurement of digital opportunity, though differences still exist in different regions. Our new DEA-OI offers much more than the ICT-OI. Using our model, the target and peer groups for each country can be identified.

Carbon tax policy is widely used to control greenhouse gases and how to determine a suitable carbon tax rate is very important for policy makers considering the trade-off between environmental protection and economic development. In an industry regulated by carbon tax policy, we consider two competing firms who sell ordinary products and green products respectively. In order to promote the firm who sells ordinary product to reduce carbon emissions, the government of China imposes carbon tax on the ordinary products. For the government, three objectives are considered when it makes carbon tax policy. They are increasing the government revenue, reducing the government expenditure and decreasing the carbon emissions. For the firms, it is important to explore their pricing strategies taken into account of the government tax policy. To find an optimal carbon tax rate and to achieve the three objectives simultaneously, we consider this as a multiple criteria decision-making problem. Hence, we propose to use a centralized data envelopment analysis (DEA) approach to solve it. We find that when one firm produces ordinary products and the other produces green products, the government may set a high tax rate. While when both firms sell ordinary products, the optimal tax policy for each firm is different and the government may impose a higher tax rate for one firm and a lower tax rate for the other firm.

It has been proven that DEA efficiencies, within the interval (0, 1], are overestimated for finite samples, while asymptotically, this bias reduces to zero. In the extant literature, the statistical inference approaches yielding the best-performing DEA estimates are the smoothed bootstrap and Bayesian DEA methods. All statistical inference techniques apply to DEA models yielding efficiencies between zero and one. This study presents a new Bayesian DEA approach that takes into account efficiencies and super-efficiencies aiming to improve bias correction. We prove that efficiencies and super-efficiencies are interrelated for finite samples. However, bias correction is statistically significant only in the case of efficiencies below one. The new Bayesian super-efficiency DEA approach yields estimates with lower mean absolute error and mean square error than the extant DEA statistical inference techniques referring only to efficiencies with right-censored distributions, where efficiencies are not allowed to exceed unity. Drawing on formal analysis, real-world and simulated data sets, we conclude that the new Bayesian super-efficiency DEA estimates are consistent of DEA parameters.

Policy makers continue to debate whether or not to increase the share of health care expenditures in United Kingdom. On the other hand, the pressure of operating within tight budgets and the advances in technology are forcing more locally based hospitals to close. One that could be used by policy makers as a benchmark is the measure of relative performance of hospitals. Many researchers have examined the source of inefficiency in health sectors (see, for example, Harris et al., Oper. Res. Soc. 57:801-811, 2000, Ozcan et al., Med. Case 30:781-784, 1992; Ozcan et aL., J. Med. Syst. 20(3)141-150, 1996; and Grosskopf and Valdmanis, J. Health. Econ. 6:89-107, 1987 but there is no evidence of measuring performance of neonatal care units of Scottish hospitals in the DEA literature. The purpose of this paper is to measure both technical and scale efficiency using data envelopment analysis in a selection of 22 neonatal care units in Scotland. The analysis suggests that major inefficiency likely exists in health care production in United Kingdom. There is potential for improving productivity by 20%.

Previous studies examining the electricity consumption behavior using traditional research methods, before the smart-meter era, mostly worked on fewer variables, and the practical implications of the findings were predominantly tailored towards suppliers and businesses rather than residents. This study first provides an overview of prior research findings on electric energy use patterns and their predictors in the pre and post smart-meter era, honing in on machine learning techniques for the latter. It then addresses identified gaps in the literature by: 1) analyzing a highly detailed dataset containing a variety of variables on the physical, demographic, and socioeconomic characteristics of households using unsupervised machine learning algorithms, including feature selection and cluster analysis; and 2) examining the environmental attitude of high consumption and low consumption clusters to generate practical implications for residents.

Auditing energy usage of farming operations is a key step towards agricultural sustainability. The current systems of tomato production use a considerable quantity of energy. As a result, improving energy efficiency is a crucial stage in decreasing energy consumption in tomato production. Data envelopment analysis (DEA) model is an established methodology to assess energy efficiency in crop production. In this study, the bounded adjusted measure (BAM) is applied for improving the efficiency of tomato production as well as decreasing the carbon footprint. In this regard, the overall, environmental, production, and pure emission efficiency of tomato production in 24 provinces of Iran are investigated. The nine overall efficient tomato producing provinces recognized that showed they had no input excesses and/or output shortfalls. Also, similar to the overall efficiency, nine out of the 24 DMUs were recognized as environmental, production, and pure emission efficient. Finally, in order to measure the probable amounts of excessive investments in inputs, with the aim of obtaining more outputs, a new approach of determining congestion is proposed based on BAM model.

Over 60% of the recurrent budget of the Ministry of Health (MoH) in Angola is spent on the operations of the fixed health care facilities (health centres plus hospitals). However, to date, no study has been attempted to investigate how efficiently those resources are used to produce health services. Therefore the objectives of this study were to assess the technical efficiency of public municipal hospitals in Angola; assess changes in productivity over time with a view to analyzing changes in efficiency and technology; and demonstrate how the results can be used in the pursuit of the public health objective of promoting efficiency in the use of health resources. The analysis was based on a 3-year panel data from all the 28 public municipal hospitals in Angola. Data Envelopment Analysis (DEA), a non-parametric linear programming approach, was employed to assess the technical and scale efficiency and productivity change over time using Malmquist index. The results show that on average, productivity of municipal hospitals in Angola increased by 4.5% over the period 2000-2002; that growth was due to improvements in efficiency rather than innovation.

Data envelopment analysis (DEA) is a well-known non-parametric technique primarily used to estimate radial efficiency under a set of mild assumptions regarding the production possibility set and the production function. The technical efficiency measure can be complemented with a consistent radial metrics for cost, revenue and profit efficiency in DEA, but only for the setting with known input and output prices. In many real applications of performance measurement, such as the evaluation of utilities, banks and supply chain operations, the input and/or output data are often stochastic and linked to exogenous random variables. It is known from standard results in stochastic programming that rankings of stochastic functions are biased if expected values are used for key parameters. In this paper, we propose economic efficiency measures for stochastic data with known input and output prices. We transform the stochastic economic efficiency models into a deterministic equivalent non-linear form that can be simplified to a deterministic programming with quadratic constraints. An application for a cost minimizing planning problem of a state government in the US is presented to illustrate the applicability of the proposed framework.

The 7th International Conference on Computer Science and Application Engineering (CSAE 2023) was successfully held online during October 17-18, 2023. It is held annually to provide a comprehensive global forum for experts and participants from academia to exchange ideas and present results of ongoing research in the most state-of-the-art areas of computer science and application engineering. There were over 50 experts and scholars from 6 countries and regions, including China, UK, Singapore, Russia, Japan, Bosnia and Herzegovina, attending the conference. Three sessions were included: keynote speeches, oral presentations and poster presentations, covering a wide range of computer science and application. There were 2 keynote speakers, 18 oral presenters and 24 poster presenters sharing their latest research results and ideas with the audiences. The proceedings of this conference include 57 accepted contributions selected from 136 submissions. We would like to express our gratitude to the reviewers of these manuscripts, who provided rational, constructive comments & suggestions to the authors, and extend our sincere thanks to the authors for their valuable contributions. Finally, our sincere gratitude goes to the ACM Publishing editors and managers for their helpful cooperation during the preparation of the conference proceedings.

The 6th International Conference on Computer Science and Application Engineering (CSAE2022) was successfully held online during October 21-22, 2022. It is held annually to providea comprehensive global forum for experts and participants from academia to exchange ideasand present results of ongoing research in the most state-of-the-art areas of computerscience and application engineering.There were about 50 experts and scholars from 5 countries and regions, including China, UK,Canada, Austria, attending the conference. Three sessions were included: keynote speeches,oral presentations and poster presentations, covering a wide range of computer science andapplication. There were 6 keynote speakers, 13 oral presenters and 35 poster presenterssharing their latest research results and ideas with the audiences. Details about thepresentations can be found in the Conference Overview part.The proceedings of this conference include 66 accepted contributions selected from 150submissions.We would like to express our gratitude to the reviewers of these manuscripts, who providedrational, constructive comments & suggestions to the authors, and extend our sincere thanksto the authors for their valuable contributions. Finally, our sincere gratitude goes to the ACMPublishing editors and managers for their helpful cooperation during the preparation of theconference proceedings.

The 3rd International Conference on Computer Science and Application Engineering (CSAE 2019) was successfully held in Sanya, China during October 22-24, 2019. The conference was to provide a high forum for researchers, scholars, and engineers in the general areas of Computer Sciences and Application Engineering to disseminate their latest research results and exchange views on the future research directions of these fields, to exchange computer science and integrate of its practice, application of the academic ideas, improve the academic depth of computer science and its application, provide an international communication platform for technology and scientific research for the world universities, business intelligence engineering field experts, professionals, and business executives.

The intensity of global competition and ever-increasing economic uncertainties has led organizations to search for more efficient and effective ways to manage their business operations. Data envelopment analysis (DEA) has been widely used as a conceptually simple yet powerful tool for evaluating organizational productivity and performance. Fuzzy DEA (FDEA) is a promising extension of the conventional DEA proposed for dealing with imprecise and ambiguous data in performance measurement problems. This book is the first volume in the literature to present the state-of-the-art developments and applications of FDEA. It is designed for students, educators, researchers, consultants and practicing managers in business, industry, and government with a basic understanding of the DEA and fuzzy logic concepts.

•Developing game-network DEA considering sustainability of transportation system.•Introducing a novel hybrid egalitarian bargaining game Data Envelopment Analysis.•Considering economic, social and environmental aspects in analyzing transportation system.•Analyzing a real case study of urban transportation network. Selecting and scheduling urban road construction projects (URCPs) is inherently an Urban Network Design Problem (UNDP) with a complex decision making process. Recently some studies have focused on sustainable UNDP, using different mathematical methods. In this paper, first a new network data envelopment analysis (NDEA) model has been developed. Then, considering sustainability dimensions, by integrating data envelopment analysis (DEA), game theory and sustainable UNDP, a bi-level model has been proposed for selecting and scheduling URCPs. A meta-heuristic algorithm is proposed to solve the presented bi-level model. Different test instances are solved to show the acceptable performance of proposed algorithm in both solution quality and execution time. Afterwards, the proposed model is applied to study the problem of urban road construction projects selection in a real-world case study of urban transportation network of Isfahan city in Iran. The results show that by applying obtained solution the environmental and social performance of the network has been improved and the performance of the network is almost efficient in all evaluation periods.

This paper highlights the role of behavioral factors for efficiency measurement in supply networks. To this aim, behavioral issues are investigated among interrelations between decision makers involved in corporate bond service networks. The corporate bond network was considered in three consecutive stages, where each stage represents the relations between two members of the network: issuer-underwriter, underwriter-bank, and bank-investor. Adopting a multi-method approach, we collected behavioral data by conducting semi-structured interviews and applying the critical incident technique. Financial and behavioral data, collected from each stage in 20 corporate bond networks, were analyzed using fuzzy network data envelopment analysis to obtain overall and stage-wise efficiency scores for each network. Sensitivity analyzes of the findings revealed inefficiencies in the relations between underwriters-issuers, banks-underwriters, and banks-investors stemming from certain behavioral factors. The results show that incorporating behavioral factors provides a better means of efficiency measurement in supply networks.

Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement. The Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis highlights the advantages of using DEA as a tool to improve business performance and identify sources of inefficiency in public and private organizations. These recently developed theories and applications of DEA will be useful for policymakers, managers, and practitioners in the areas of sustainable development of our society including environment, agriculture, finance, and higher education sectors. Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement. The Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis highlights the advantages of using DEA as a tool to improve business performance and identify sources of inefficiency in public and private organizations. These recently developed theories and applications of DEA will be useful for policymakers, managers, and practitioners in the areas of sustainable development of our society including environment, agriculture, finance, and higher education sectors.

Data Envelopment Analysis (DEA) methods have been widely used in many fields, including operations research, optimization, operations management, industrial engineering, accounting, management, and economics. This chapter starts with an introduction to common DEA-based models in the envelopment and multiplier forms to illustrate the importance of these models. Then, we provide details of the recent theoretical developments including Network DEA, Stochastic DEA, Fuzzy DEA, Bootstrapping, Directional measures, desirable (good) and undesirable (bad) factors, and Directional returns to scale. This is followed by the presentation of some novel applications of DEA to provide direction for future developments in this field. In summary, this chapter aims to discuss some of the latest developments in DEA and provide direction for future research.

The aim of this paper is to illustrate the measurement of productive efficiency using Nerlovian indicator and metafrontier with data envelopment analysis techniques. Further, we illustrate how profit efficiency of firms operating in different regions can be aggregated into one overarching frontier. Sugarcane production in three regions in Kenya has been used to illustrate these concepts. Results show that the sources of inefficiency in all regions are both technical and allocative, but allocative efficiency contributes more to the overall Nerlovian (in) efficiency indicator.

While conventional Data Envelopment Analysis (DEA) models set targets for each operational unit, this paper considers the problem of input/output reduction in a centralized decision making environment. The purpose of this paper is to develop an approach to input/output reduction problem that typically occurs in organizations with a centralized decision-making environment. This paper shows that DEA can make an important contribution to this problem and discusses how DEA-based model can be used to determine an optimal input/output reduction plan. An application in banking sector with limitation in IT investment shows the usefulness of the proposed method. (C) 2011 Elsevier B.V. All rights reserved.

Data Envelopment Analysis (DEA) model has been applied for evaluating bank branches and recognizing efficient and inefficient branches can help bank managers to provide appropriate strategies to improve the inefficient branches' performance. Conventional DEA models are based on the " black box " approach. However, the process of providing services in banks is made up of interactive and interdependent processes. Additionally, some managers tend to incorporate their preferences in evaluation process. In this paper, Best Worst Method (BWM) is used for incorporating decision maker (DM) preferences in two-stage DEA model. First, BWM model is applied to obtain the weights of inputs, intermediate measures and outputs based on decision maker's (DM) judgment. Second, generated weights are imposed on two-stage DEA model as additional constraints and a novel bi-objective BWM-two stage DEA model is introduced. Finally, the proposed bi-objective BWM-two stage DEA model is solved using min-max approach. To illustrate the capability of proposed model, 45 Agricultural Bank (Agribank) branches in West Azerbaijan province of Iran are evaluated. The branches' processes are considered as two stages " production " and " profitability " and efficiency of branches are calculated in each stage. According to the efficiencies of each sub-stage, branches are divided to four groups and recommendations are made for each group.

Incorporating Material Balance Principle (MBP) in industrial and agricultural performance measurement systems with pollutant factors has been on the rise in recent years. Many conventional methods of performance measurement have proven incompatible with the material flow conditions. This study will address the issue of eco-efficiency measurement adjusted for pollution, taking into account materials flow conditions and the MBP requirements, in order to provide 'real' measures of performance that can serve as guides when making policies. We develop a new approach by integrating slacks-based measure to enhance the Malmquist Luenberger Index by a material balance condition that reflects the conservation of matter. This model is compared with a similar model, which incorporates MBP using the trade-off approach to measure productivity and eco-efficiency trends of power plants. Results reveal similar findings for both models substantiating robustness and applicability of the proposed model in this paper.

For a submitted query to multiple search engines finding relevant results is an important task. This paper formulates the problem of aggregation and ranking of multiple search engines results in the form of a minimax linear programming model. Besides the novel application, this study detects the most relevant information among a return set of ranked lists of documents retrieved by distinct search engines. Furthermore, two numerical examples aree used to illustrate the usefulness of the proposed approach.

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach. (C) 2013 Elsevier B.V. All rights reserved.

Analysis of the production efficiency of industrialized countries, questioning whether certain countries perform better than others in producing more output with the same or less inputs, is an example of the importance of estimating production relationships. In order to estimate efficiency one needs an appropriate model for the two major inputs into production activity, namely labour and capital. A physical asset once installed is capable of contributing several years of output. This implies that investments made in previous years must be taken into account in order to produce a measure of the efficiency and productivity for any given year. The purpose of this article is to introduce a dynamic efficiency model and compare the results with previous work on the analysis of efficiency and productivity of OECD countries. The article proposes that dynamic models capture efficiency better than static models.

"This book highlights the advantages of using data envelopment analysis as a tool to improve business performances and identifying the source of inefficiency in public and private organizations"--Provided by publisher. Includes bibliographical references.

This paper explores the use of the optimization procedures in SAS/OR software with application to the ordered weight averaging (OWA) operators of decision-making units (DMUs). OWA was originally introduced by Yager (IEEE Trans Syst Man Cybern 18(1):183–190, 1988 ) has gained much interest among researchers, hence many applications such as in the areas of decision making, expert systems, data mining, approximate reasoning, fuzzy system and control have been proposed. On the other hand, the SAS is powerful software and it is capable of running various optimization tools such as linear and non-linear programming with all type of constraints. To facilitate the use of OWA operator by SAS users, a code was implemented. The SAS macro developed in this paper selects the criteria and alternatives from a SAS dataset and calculates a set of OWA weights. An example is given to illustrate the features of SAS/OWA software.

Data envelopment analysis (DEA) has gained a wide range of applications in measuring comparative efficiency of decision making units (DMUs) with multiple incommensurate inputs and outputs. The standard DEA method requires that the status of all input and output variables be known exactly. However, in many real applications, the status of some measures is not clearly known as inputs or outputs. These measures are referred to as flexible measures. This paper proposes a flexible slacks-based measure (FSBM) of efficiency in which each flexible measure can play input role for some DMUs and output role for others to maximize the relative efficiency of the DMU under evaluation. Further, we will show that when an operational unit is efficient in a specific flexible measure, this measure can play both input and output roles for this unit. In this case, the optimal input/output designation for flexible measure is one that optimizes the efficiency of the artificial average unit. An application in assessing UK higher education institutions used to show the applicability of the proposed approach. (C) 2013 Elsevier Ltd. All rights reserved.

This book is the first in the literature to present the state of the art and some interesting and relevant applications of the Fuzzy Analytic Hierarchy Process (FAHP). The AHP is a conceptually and mathematically simple, easily implementable, yet extremely powerful tool for group decision making and is used around the world in a wide variety of decision situations, in fields such as government, business, industry, healthcare, and education. The aim of this book is to study various fuzzy methods for dealing with the imprecise and ambiguous data in AHP. Features: First book available on FAHP. Showcases state-of-the-art developments. Contains several novel real-life applications. Provides useful insights to both academics and practitioners in making group decisions under uncertainty This book provides the necessary background to work with existing fuzzy AHP models. Once the material in this book has been mastered, the reader will be able to apply fuzzy AHP models to his or her problems for making decisions with imprecise data.

In this paper we propose a data envelopment analysis (DEA) based method for assessing the comparative efficiencies of units operating production processes where input–output levels are inter-temporally dependent. One cause of inter-temporal dependence between input and output levels is capital stock which influences output levels over many production periods. Such units cannot be assessed by traditional or ‘static’ DEA which assumes input–output correspondences are contemporaneous in the sense that the output levels observed in a time period are the product solely of the input levels observed during that same period. The method developed in the paper overcomes the problem of inter-temporal input–output dependence by using input–output ‘paths’ mapped out by operating units over time as the basis of assessing them. As an application we compare the results of the dynamic and static model for a set of UK universities. The paper is suggested that dynamic model capture the efficiency better than static model.

The major aim of this research is benchmarking top Arab banks using Data Envelopment Analysis (DEA) technique and to compare the results with that of published recently in Mostafa (2007a,b) [Mostafa, M, M. (2007a). Modeling the efficiency of top Arab banks: A DEA-neural network approach. Expert Systems with Applications, doi:10.1016/j.eswa.2007.09.001: Mostafa M. M. (2007b), Benchmarking top Arab banks' efficiency through efficient frontier analysis, Industrial Management & Data Systems, 107(6) 802-823]. Data for 85 Arab banks used to conduct the analysis of relative efficiency. Our findings indicate that (1) the efficiency of Arab banks reported in Mostafa (2007a,b) is incorrect. hence, readers should take extra caution of using such results, (2) the corrected efficiency scores suggest that there is potential for significant improvements in Arab banks. In summary, this study overcomes with some data and methodology issues in measuring efficiency of Arab banks and highlights the importance of encouraging increased efficiency throughout the banking industry in the Arab world using the new results. (C) 2008 Elsevier Ltd. All rights reserved.

•Presents a novel fuzzy DEA based on a general fuzzy measure.•Develops an adjustable and flexible fuzzy DEA model to consider DMUs’ preferences.•Applying the adjustable FDEA model for measuring efficiency of hospitals in USA. Possibilistic Data Envelopment Analysis (PDEA) is one of the most applicable and popular approaches in the literature to deal with imprecise and ambiguous data in DEA models. In this approach, with respect to tendency of decision maker (DM) in taking optimistic, pessimistic and compromise attitude, three measures including possibility, necessity and credibility measures are used to form the Fuzzy DEA (FDEA) models, respectively. However, decision makers may have different preference and so it is necessary to customize fuzzy DEA models according to properties of DMUs. This paper proposes a novel fuzzy DEA model based on general fuzzy measure in which the attitude of DMUs could be determined by the optimistic-pessimistic parameters. As a result, the proposed FDEA model is general, applicable, flexible, and adjustable based on each DMUs. A numerical example is used to explain the proposed approach while usefulness and applicability of this approach have been illustrated using a real data set to measure efficiency of 38 hospital in United States.

In era of reglobalization, sustainably resilient supply chains (SCs) are imperative in corporations to improve performance and meet stockholders' expectations. However, sustainably resilient SCs could not be effective if are not assessed by using advanced frameworks, systems , and models. As such, developing a novel network data envelopment model (DEA) to appraise sustainably resilient SCs is our purpose in this article. To do so, we present a new double-frontier methodology to provide optimistic and pessimistic efficiency measures in network structures. Moreover, ideas of outputs weak disposability, chance-constrained programming , and discrete dominance are incorporated in a unified framework of modelling efficient and inefficient production technologies. The new network DEA model also can address dissimilar types of data, including undesirable and integer-valued and ratio outputs, stochastic intermediate products, and integer-valued inputs in a unified framework. Furthermore , an aggregated Farrell type efficiency measure is developed which allows to provide the complete ranking of units so that each decision-making unit (DMU) has its own rank in both overall and divisional point of view. We show the unique features of our developed model using a real case study in paint industry to evaluate the efficiency and reducing carbon dioxide (CO2) emissions. The results show that how well the proposed models can evaluate the sustainability and resilience of supply chains in the presence of uncertainty and with dissimilar types of data.

This paper surveys the increasing use of statistical approaches in non-parametric efficiency studies. Data Envelopment Analysis (DEA) and Free Disposable Hull (FDH) are recognized as standard non-parametric methods developed in the field of operations research. Kneip et al. (1998) and Park et al. (2000) develop statistical properties of the variable returns-to-scale (VRS) version of DEA estimators and FDH estimators, respectively. Simar & Wilson (1998) show that conventional bootstrap methods cannot provide valid inference in the context of DEA or FDH estimators and introduce a smoothed bootstrap for use with DEA or FDH efficiency estimators. By doing so, they address the main drawback of non-parametric models as being deterministic and without a statistical interpretation. Since then, many articles have applied this innovative approach to examine efficiency and productivity in various fields while providing confidence interval estimates to gauge uncertainty. Despite this increasing research attention and significant theoretical and methodological developments in its first two decades, a specific and comprehensive bibliometric analysis of bootstrap DEA/FDH literature and subsequent statistical approaches is still missing. This paper thus, aims to provide an extensive overview of the key articles and their impact in the field. Specifically, in addition to some summary statistics such as citations, the most influential academic journals and authorship network analysis, we review the methodological developments as well as the pertinent software applications.

Determining the Ordered Weighted Averaging (OWA) operator weights is important in decision making applications. Several approaches have been proposed in the literature to obtain the associated weights. This paper provides an alternative disparity model to identify the OWA operator weights. The proposed mathematical model extends the existing disparity approaches by minimizing the sum of the deviation between two distinct OWA weights. The proposed disparity model can be used for a preference ranking aggregation. A numerical example in preference ranking and an application in search engines prove the usefulness of the generated OWA weights. (C) 2009 Elsevier Inc. All rights reserved.

Microfinance has been developed as alternative solution for global poverty alleviation effort in the last 30 years. Microfinance institution (MFI) has unique characteristic wherein they face double bottom line objectives of outreach to the poor and financial sustainability. This study proposes a two-stage analysis to measure Islamic Microfinance institutions (IMFIs) performance by comparing them to conventional MFIs. First, we develop a Data Envelopment Analysis (DEA) framework to measure MFIs' efficiency in its double bottom line objectives, i.e. in terms of social and financial efficiency. In the second stage non-parametric tests are used to compare the performance and identify factors that contribute to the efficiency of IMFIs and MFIs. (C) 2015 Elsevier Ltd. All rights reserved.

Over the last two decades, application of Data envelopment analysis (DEA) in transportation problems have gained considerable research attention. This paper presents a literature review and classification of the applications of DEA in transportation systems (TSs). First by classifying 40 papers from 2007 to 2018, the origins of DEA in transportation problems have been reviewed. Then the development and an overall view of DEA applications in TSs have been presented. We have classified the applications of DEA into six different contexts. In each context, published papers have deeply been analyzed. Content of analysis includes "Number of published papers during the time", "target journals", "countries", "keyword frequency", "most cited papers", "map of most co-cited publications". More important, we reported the "inputs and outputs variables" used in each paper. Further "a review of the selected papers" and "gaps/future research directions" have been given within each cluster. The results show that DEA is one of the most useful approach in evaluating TSs for policy makers. On the other hand, DEA can help the decision makers in transportation especially regarding environmental factors, sustainable development and eco-design. Finally, we proposed subjects for future researches including guidance for new studies in the field of DEA applications in TSs.

In Sub-Saharan Africa (SSA), there is a huge knowledge gap of health facilities performance. The objective of this study is to measure relative technical efficiencies of 54 public hospitals in Kenya using Data Envelopment Analysis (DEA) technique. 14 (26%) of the public hospitals were found to be technically inefficient. The study singled out the inefficient hospitals and provided the magnitudes of specific input reductions or output increases needed to attain technical efficiency.[PUBLICATION ABSTRACT]

In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided. (C) 2017 The Authors. Published by Elsevier Ltd.

Data Envelopment Analysis (DEA) has been widely applied in measuring the efficiency of Decision-Making Units (DMUs). The conventional DEA has three major drawbacks: a) it does not consider Decision Makers' (DMs) preferences in the evaluation process, b) DMUs in this model are flexible in weighting the criteria to reach the maximum possible efficiency, and c) it ignores the uncertainty in data. However, in many real-world applications, data are uncertain as well as imprecise and managers want to impose their opinions in decision-making procedure. To address these problems, this paper develops a novel multi-objective Best Worst Method (BWM)Robust DEA (RDEA) for incorporating DMs' preferences into DEA model in an uncertain environment. The proposed model tries to provide a new efficiency score which is more reliable and compatible with real problems by taking the advantages of the BWM to apply experts' opinions and RDEA to model the uncertainty This biobjective BWM-RDEA model is solved utilizing amin-max technique and so as to illustrate its usefulness, this model is implemented for assessing Iranian airlines.

Several network-data envelopment analysis (DEA) performance assessment models have been proposed in the literature; however, the conflicts between stages and insufficient number of decision-making units (DMUs) challenge the researchers. In this paper, a novel game-DEA model is proposed for efficiency assessment of network structure DMUs. We propose a two-stage modeling, where in the first stage network is divided into several sub-networks; we at the same time categorize input variables to measure efficiency of sub-networks within each input category. In the second stage, we calculate efficiency of the network by aggregating efficiency scores of sub-networks within each category. In this way, the issue of insufficient number of DMUs when there are many input/output variables can be handled as well. One of the main contributions of this paper is assuming each category and stage as a player in Nash bargaining game. Using the concept borrowed from Nash bargaining game model, the proposed game-DEA model tries to maximize distances of efficiency scores of each player form their corresponding breakdown points. The usefulness of the model is presented using a real case study to measure the efficiency of bank branches.

Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement. The Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis highlights the advantages of using DEA as a tool to improve business performance and identify sources of inefficiency in public and private organizations. These recently developed theories and applications of DEA will be useful for policymakers, managers, and practitioners in the areas of sustainable development of our society including environment, agriculture, finance, and higher education sectors. Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement. The Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis highlights the advantages of using DEA as a tool to improve business performance and identify sources of inefficiency in public and private organizations. These recently developed theories and applications of DEA will be useful for policymakers, managers, and practitioners in the areas of sustainable development of our society including environment, agriculture, finance, and higher education sectors.

Incorporating further information into the ordered weighted averaging (OWA) operator weights is investigated in this paper. We first prove that for a constant orness the minimax disparity model [13] has unique optimal solution while the modified minimax disparity model [16] has alternative optimal OWA weights. Multiple optimal solutions in modified minimax disparity model provide us opportunity to define a parametric aggregation OWA which gives flexibility to decision makers in the process of aggregation and selecting the best alternative. Finally, the usefulness of the proposed parametric aggregation method is illustrated with an application in metasearch engine. (C) 2011 Elsevier Inc. All rights reserved.

Classical data envelopment analysis models have been applied to extract efficiency when time series data are used. However, these models do not always yield realistic results, especially when the purpose of the study is to identify the peers of the decision making unit (DMU) under investigation. This is due to the fact that apart from the spatial distance of DMUs, which is the basis on which efficiency is extracted, the distance in time between DMUs is also important in identifying the most suitable peer that could serve as a benchmark for the DMU under investigation. Based on these two dimensions, i.e. the spatial and the temporal, the concept of spatio-temporal efficiency is introduced and a mixed integer linear programming model is proposed to obtain its value. This model yields a unique past peer for benchmarking purposes based on both dimensions. The implementation has been performed in the R language, where the user can provide, through a graphical interface, the data (inputs and outputs for successive versions of a DMU) for which the spatio-temporal efficiency is measured. Applications to the real world and particularly from the discipline of software engineering are provided to show the applicability of the model to temporally arranged data. Profiling results of the code in the R language are also provided showing the effectiveness of the implementation.

Selecting the best alternative in a group decision making is a subject of many recent studies The most popular method proposed for ranking the alternatives is based on the distance of each alternative to the ideal alternative. The ideal alternative may never exist, hence the ranking results are biased to the ideal point. The main aim in this study is to calculate a fuzzy ideal point that is more realistic to the crisp ideal point On the other hand, recently Data Envelopment Analysis (DEA) is used to find the optimum weights for ranking the alternatives. This paper proposes a four stage approach based on DEA in the Fuzzy environment to aggregate preference rankings An application of preferential voting system shows how the new model can be applied to rank a set of alternatives. Other two examples indicate the priority of the proposed method compared to the some other suggested methods Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved

The conventional Total Factor Productivity (TFP) measurement does not incorporate the effects of undesirable outputs, which are harmful to the environment. Using sugarcane farming in Kenya, this paper illustrates the differences between the conventional Malmquist index measures where the environment variable is not adjusted and environment-adjusted measures using both hyperbolic and directional distance functions. The mean TFP change estimates for the conventional Malmquist index, adjusted hyperbolic index and Luenberger indicator were 3.13%, 0.11% and 2.21%, respectively. The conventional non-adjusted measure lies between the two adjusted measures of hyperbolic index and Luenberger indicator.

The key consideration for firms' restructuring is improving their operational efficiencies. Market conditions often offer opportunities or generate threats that can be handled by restructuring scenarios through consolidation, to create synergy, or through split, to create reverse synergy. A generalized restructuring refers to a move in a business market where a homogeneous set of firms, a set of pre-restructuring decision making units (DMUs), proceed with a restructuring to produce a new set of post-restructuring entities in the same market to realize efficiency targets. This paper aims to develop a novel inverse Data Envelopment Analysis based methodology, called GInvDEA (Generalized Inverse DEA), for modeling the generalized restructuring. Moreover, the paper suggests a linear programming model that allows determining the lowest performance levels, measured by efficiency that can be achieved through a given generalized restructuring. An application in banking operations illustrates the theory developed in the paper.

Data analytics projects can be like throwing darts in the dark. Problem‐centric thinking is vital, argue Vincent Charles, Ali Emrouznejad, Tatiana Gherman, and James Cochran Data analytics projects can be like throwing darts in the dark. Problem‐centric thinking is vital, argue Vincent Charles, Ali Emrouznejad, Tatiana Gherman, and James Cochran

The main advantage of Data Envelopment Analysis (DEA) is that it does not require any priori weights for inputs and outputs and allows individual DMUs to evaluate their efficiencies with the input and output weights that are only most favorable weights for calculating their efficiency. It can be argued that if DMUs are experiencing similar circumstances, then the pricing of inputs and outputs should apply uniformly across all DMUs. That is using of different weights for DMUs makes their efficiencies unable to be compared and not possible to rank them on the same basis. This is a significant drawback of DEA; however literature observed many solutions including the use of common set of weights (CSW). Besides, the conventional DEA methods require accurate measurement of both the inputs and outputs; however, crisp input and output data may not relevant be available in real world applications. This paper develops a new model for the calculation of CSW in fuzzy environments using fuzzy DEA. Further, a numerical example is used to show the validity and efficacy of the proposed model and to compare the results with previous models available in the literature.

•We introduce a cubic spline interpolation function to fit personalized quantifier.•We employ the probabilistic linguistic term sets to express evaluations.•We illustrate the usability of the proposed mdoel in blockchain risk evaluation. With the applications of blockchain technology in various fields, the research on blockchain has attracted much attention. Different from the researches focusing on specific applications of blockchain technology in a certain field, this study devotes to capturing the attitudes of investors regarding different risk criteria in blockchain technology investment decision making. We use personalized quantifiers to extract investors’ preferences on each risk evaluation criterion. At present, the personalized quantifier that can reflect individual attitudes and behavior intentions have been fitted by various functions, but there are still limitations. In this regard, this paper introduces a cubic spline interpolation function to fit the personalized quantifier, and addresses the consistency of the personalized quantifier in the ordered weighted averaging aggregation. Moreover, we employ a qualitative information representation model called probabilistic linguistic term sets to express decision-makers' evaluations on each criterion. We give a case study to illustrate the usability of the proposed personalized quantifier in blockchain risk evaluation. The comparative analysis with other four types of personalized quantifiers shows that our proposed personalized quantifier with cubic spline interpolation has ideal geometric characteristics in terms of smooth curve and high fitting accuracy, thus having strong applicability. Further, we show that this method is relatively easy to operate.

In the traditional TOPSIS, the ideal solutions are assumed to be located at the endpoints of the data interval. However, not all performance attributes possess ideal values at the endpoints. We termed performance attributes that have ideal values at extreme points as Type-1 attributes. Type-2 attributes however possess ideal values somewhere within the data interval instead of being at the extreme end points. This provides a preference ranking problem when all attributes are computed and assumed to be of the Type-1 nature. To overcome this issue, we propose a new Fuzzy DEA method for computing the ideal values and distance function of Type-2 attributes in a TOPSIS methodology. Our method allows Type-1 and Type-2 attributes to be included in an evaluation system without compromising the ranking quality. The efficacy of the proposed model is illustrated with a vendor evaluation case for a high-tech investment decision making exercise. A comparison analysis with the traditional TOPSIS is also presented.

China has achieved significant progress in terms of economic and social developments since implementation of reform and open policy in 1978. However, the rapid speed of economic growth in China has also resulted in high energy consumption and serious environmental problems, which hindering the sustainability of China's economic growth. This paper provides a framework for measuring eco-efficiency with CO2 emissions in Chinese manufacturing industries. We introduce a global Malmquist-Luenberger productivity index (GMLPI) that can handle undesirable factors within Data Envelopment Analysis (DEA). This study suggested after regulations imposed by the Chinese government, in the last stage of the analysis, i.e. during 2011–2012, the contemporaneous frontier shifts towards the global technology frontier in the direction of more desirable outputs and less undesirable outputs, i.e. producing less CO2 emissions, but the GMLPI drops slightly. This is an indication that the Chinese government needs to implement more policy regulations in order to maintain productivity index while reducing CO2 emissions. •Introducing a non-parametric framework for reducing CO2 emissions.•Developing a new global Malmquist-Luenberger productivity index that can handle undesirable factors within DEA.•Analysing Chineses manufacturing in order to maintain productivity index while reducing CO2 emission.

Fare, Grosskopf, Norris and Zhang developed a non-parametric productivity index, Malmquist index, using data envelopment analysis (DEA). The Malmquist index is a measure of productivity progress (regress) and it can be decomposed to different components such as 'efficiency catch-up' and 'technology change'. However, Malmquist index and its components are based on two period of time which can capture only a part of the impact of investment in long-lived assets. The effects of lags in the investment process on the capital stock have been ignored in the current model of Malmquist index. This paper extends the recent dynamic DEA model introduced by Emrouznejad and Thanassoulis and Emrouznejad for dynamic Malmquist index. This paper shows that the dynamic productivity results for Organisation for Economic Cooperation and Development countries should reflect reality better than those based on conventional model.

Big Data, perceived as one of the breakthrough technological developments of our times, has the potential to revolutionize essentially any area of knowledge and impact on any aspect of our life. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, analysts, researchers, and business users can analyze previously inaccessible or unusable data to gain new insights resulting in better and faster decisions, and producing both economic and social value; it can have an impact on employment growth, productivity, the development of new products and services, traffic management, spread of viral outbreaks, and so on. But great opportunities also bring great challenges, such as the loss of individual privacy. In this chapter, we aim to provide an introduction into what Big Data is and an overview of the social value that can be extracted from it; to this aim, we explore some of the key literature on the subject. We also call attention to the potential 'dark' side of Big Data, but argue that more studies are needed to fully understand the downside of it. We conclude this chapter with some final reflections.

► We develop a DEA model for cases that the input–output variables cannot be precisely measured. ► The concept of “local α-level” for measuring efficiency of DMUs under uncertainty is introduced. ► The efficiency of DMUs in fuzzy assessment is measured by a multi-objective programming. ► The application clearly showed a better estimation of efficiency when using the proposed model. Data envelopment analysis (DEA) as introduced by Charnes, Cooper, and Rhodes (1978) is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input–output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on α-cut. One drawback of the α-cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the α-cut approach. We introduce the concept of “local α-level” to develop a multi-objective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.

In this study, we developed a DEA-based performance measurement methodology that is consistent with performance assessment frameworks such as the Balanced Scorecard. The methodology developed in this paper takes into account the direct or inverse relationships that may exist among the dimensions of performance to construct appropriate production frontiers. The production frontiers we obtained are deemed appropriate as they consist solely affirms with desirable levels for all dimensions of performance. These levels should be at least equal to the critical values set by decision makers. The properties and advantages of our methodology against competing methodologies are presented through an application to a real-world case study from retail firms operating in the US. A comparative analysis between the new methodology and existing methodologies explains the failure of the existing approaches to define appropriate production frontiers when directly or inversely related dimensions of performance are present and to express the interrelationships between the dimensions of performance. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

This study analyzes the efficiency levels of the banking industry in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 2010 to 2014, using an integrated two-stage fuzzy approach. Very often the reliability of data collected from BRICS is questionable. In this research, we first use fuzzy TOPSIS to capture vagueness in the relative efficiency of BRICS banking over time. In the second stage, we adopt fuzzy regressions based on different rule-based systems to enhance the power of significant socioeconomic, regulatory, and demographic variables to predict banking efficiency. These variables are previously identified by using bootstrapped truncated regressions with conditional alpha-levels, as proposed by Wanke, Barros, and Emrouznejad (2015a). The results reveal that efficiency in the banking industry is positively associated with country gross savings and the GINI index ratio, but negatively associated with relatively high inflation ratios. Fuzzy regressions proved far more accurate than bootstrapped truncated regressions with conditional a-levels. We derive policy implications.

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios.

Sensitivity analysis establishes priorities for research and allows to identify and rank the most important factors which lead to great improvements in output factors. The aim of this study is to examine sensitivity analysis of inputs in grape production. We are proposing to perform sensitivity analysis using partial rank correlation coefficient (PRCC) which is the most reliable and efficient method, and we apply this for the first time in crop production. This research investigates the use of energy in the vineyard of a semi-arid zone of Iran. Energy use efficiency, energy productivity, specific energy and net energy were calculated. Various artificial neural network (ANN) models were developed to predict grape yield with respect to input energies. ANN models consist of a multilayer perceptron (MLP) with seven neurons in the input layer, one and two hidden layer(s) with different number of neurons, and an output layer with one neuron. Input energies were labor, machinery, chemicals, farmyard manure (FYM), diesel, electricity and water for irrigation. Sensitivity analysis was performed on over 100 samples of parameter space generated by Latin hypercube sampling method, which was then fed to the ANN model to predict the yield for each sample. The PRCC between the predicted yield and each parameter value (input) was used to calculate the sensitivity of the model to each input. Results of sensitivity analysis showed that machinery had the greatest impact on grape yield followed by diesel fuel and labor. (C) 2018 The Authors. Published by Elsevier Ltd.

Data Envelopment Analysis has been widely used to analyze the efficiency of health sector in developed countries, since 1978, while in Africa, only a few studies have attempted to apply DEA in the health organizations. In this paper we measure technical efficiency of public health centers in Kenya. Our finding suggests that 44% of public health centers are inefficient. Therefore, the objectives of this study are: to determine the degree of technical efficiency of individual primary health care facilities in Kenya; to recommend the performance targets for inefficient facilities; to estimate the magnitudes of excess inputs; and to recommend what should be done with those excess inputs. The authors believe that this kind of studies should be undertaken in the other countries in the World Health Organization (WHO) African Region with a view to empowering Ministries of Health to play their stewardship role more effectively.[PUBLICATION ABSTRACT]

In this paper, we developed an alternative Network Slacks-Based Data Envelopment Analysis Measure (NSBM) wherein the overall efficiency is expressed as a weighted average of the efficiencies of the individual processes. The advantage of this new model is that both overall efficiency and multi-divisional efficiencies have been calculated with a unified framework. The major merits of the proposed model are its ability to provide appropriate measure of efficiency, obtaining weight of processes from model, simultaneous assessment of intermediate variables considering them as both input and output. Finally, an application in electric power companies shows the practicality of the proposed model. •Developing network DEA model to decompose the system efficiency.•Pareto target estimation for intermediate products of electric power companies.•The transmission process is more efficient than generation & distribution.•To improve the efficiency, policy makers should make more attention to the generation process.

Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we compare Stochastic-DEA and Fuzzy-DEA models to assess, respectively, how the underlying randomness and fuzziness impact efficiency levels. The proposed models have been demonstrated using an application in Angolan banks. Findings reveal that conclusions with respect to the ranking of DMUs may vary substantially depending upon the type of the model chosen, although efficiency scores are similar to some extent when compared within the ambits of Stochastic-DEA and Fuzzy-DEA models. Additionally, modeling choices on fuzziness, rather than on randomness, appears to be the most critical source for variations in efficiency rankings. Managerial implications for Angolan banks are also explored.

Data Envelopment Analysis (DEA) is recognized as a modern approach to the assessment of performance of a set of homogeneous Decision Making Units (DMUs) that use similar sources to produce similar outputs. While DEA commonly is used with precise data, recently several approaches are introduced for evaluating DMUs with uncertain data. In the existing approaches many information on uncertainties are lost. For example in the defuzzification, the α -level and fuzzy ranking approaches are not considered. In the tolerance approach the inequality or equality signs are fuzzified but the fuzzy coefficients (inputs and outputs) are not treated directly. The purpose of this paper is to develop a new model to evaluate DMUs under uncertainty using Fuzzy DEA and to include α -level to the model under fuzzy environment. An example is given to illustrate this method in details.

Composite indices are used by national and international organisations, as well as governments and corporations, to track various performance aspects of a country's economy and its people, evaluate progress, and engage constructively in policy dialogue; and they have long proven useful as communication tools and inputs into decision-making and policymaking. Modern Indices for International Economic Diplomacy compiles a spectrum of relevant indices for development and well-being used in benchmarking across nations, namely the OECD Better Life Index, the Gini Index, the Gender Equality/Inequality Index, the International Energy Security Risk Index, the Big Mac Index, the Country Risk Index, the Corruption Perceptions Index, and the Global Terrorism Index. The book will be relevant to practitioners, policymakers, researchers, and students interested in the topic of international economic relationships.

Manufacturing is a major source of energy consumption and, therefore, a significant contributor to emissions and greenhouse gases. This paper is concerned with evaluating different scheduling policies in a job shop system where energy-efficient scheduling is incorporated with multiple other scheduling criteria. In the production systems being investigated, the electrical energy is offered on a time-of-use (TOU) pricing regime. The objective of minimizing TOU energy costs conflicts sharply with most other traditional objectives in production scheduling. The aim is to identify best performing scheduling rules for different scenarios based on different shop congestion levels, and devise new rules to enable an improved integration of energy cost with other scheduling criteria. A ranking approach based on data envelopment analysis (DEA) and Ordered Weighting Average (OWA) concepts is presented. The proposed methodology exploits the preference voting system embedded under the cross-efficiency (CE) matrix to derive a collective importance scale for the aggregation process. The approach is applied to 28 dispatching rules (DRs) for scheduling jobs that arrive continuously at random points in time during the production horizon. Computational results highlight the effect of energy costs on the overall ranking of the DRs, and unveil the superiority of certain rules under multi-objective performance criteria.

The airline industry is one of the major industries having a significant role in the economic development of a country, on both domestic and international sides. Hence, it is important to have the airlines performing efficiently, as much as possible. To this end, it seems necessary to continuously evaluate the performance of the airlines to find any possible chance to improve their performance. In this study, by combing the ideas of the Egalitarian Bargaining game theory, Network Data Envelopment Analysis (NDEA), and Slack-Based Measure (SBM), a new game-SBM-NDEA model has been proposed to evaluate the performance of the Decision Making Units (DMUs) with a series network structure. In addition to handling the between-stages conflict in the network structures, the proposed model can provide more reliable efficiency scores when the number of the DMUs is not large enough compared to the number of considered inputs and outputs. The developed model and Malmquist Index have been applied to analyze the performance of Iranian domestic airlines over an 8-years period from 2013 to 2020, as a real-world case study. The obtained results for overall efficiency scores, operational efficiency, service efficiency, slack/surplus values for all inputs and outputs, and efficiency changes over time have been comprehensively analyzed in order to obtain the deficiencies of each airline and find possible solutions to improve their performance.

Data Envelopment Analysis (DEA) is a mathematical programming model that calculates the relative efficiency of homogenous Decision Making Units (DMUs). The conventional DEA models used to calculate the efficiency require the exact amount of inputs and outputs; in real business situations, however, it is often impossible to determine the exact numeral value of some inputs and outputs. At the same time the Common Set of Weights (CSW) overcomes the weakness of DEA models for assessment under same conditions. On the other hands, it is important to considering the balance in evaluation and calculation of indicators. This study develops a new model to calculate the CSW in fuzzy environments, considering the balanced environment using the Balanced Scorecard (BSC). Our proposed model is linear for fairly and equitably evaluating the DMUs on the same scale, also enables us to deal with fuzzy environment and greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in fuzzy DEA models. From a managerial point of view, this paper aims to provide an integrated framework to form a better strategic decision-making process about organization performance, which ultimately leads to the competitive advantages and success of the organization in the long run. Finally, in the field of performance management, the proposed model was applied to evaluate the performances of ten manufacturing enterprises in to confirm the validity and applicability of the proposed approach.

An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements. To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, the non-oriented network slacks-based measure (SBM) model and conventional SBM model with the assumptions of constant returns to scale (CRS) and variable returns to scale (VRS) are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.

Background: Paying particular attention to sustainable food consumption in low-income households is essential for increasing human health. Due to the growing population globally, this concept will likely become more serious soon. Methods: Following the importance of optimizing food consumption for sustainability, in this study, a novel methodology is introduced for calculating nutrient intake efficiency and determining choices of food in different locations. The impact of socio-economic factors on nutrition efficiency is assessed. Data Envelopment Analysis (DEA) as a well-known linear programming (LP) and a Tobit model are used to achieve the goals. Household Consumption and Expenditure Surveys (HCESs) of 30,000 rural and urban Iranian households in all provinces in 2016 are analyzed. A Nutrient Efficiency Map (NEM) of Iran was depicted by GIS software. Results: The results showed that many townships had nutrient efficiency scores of less than 70%. Northeast townships had the lowest scores, with an efficiency score of less than 50%. Overall, townships have lower efficiency in the North (seaside cities), East (desert cities), and North East (isolated cities) when compared with other areas. Conclusion: Therefore, it is suggestible that the government should modify the support policies and the protection packages based on social, geographical, and cultural status.

The field of service operations management has a plethora of research opportunities to capitalise on, which are nowadays heightened by the presence of big data. In this research, we review and analyse the current state-of-the-art of the literature on big data for service operations management. To this aim, we use the Scopus database and the VOSviewer visualisation software for bibliometric analysis to highlight developments in research and application. Our analysis reveals patterns in scientific outputs and serves as a guide for global research trends in big data for service operations management. Some exciting directions for the future include research on building big data-driven analytical models which are deployable in the Cloud, as well as more interdisciplinary research that integrates traditional modes of enquiry with for example, behavioural approaches, with a blend of analytical and empirical methods.

Studies have shown that the sensible operation of big data may yield powerful insights that can improve the organisations’ strategic decision-making process and contribute to achieving an enhanced competitive advantage. In this manuscript, we explore the promise of big data in redefining strategy in service operations management (SOM) by means of investigating a rich range of bibliographic material. The SOM field has a plethora of research opportunities to capitalise on, which are enhanced by the presence of big data. SOM research in the big data age implies a shift in attention from being increasingly integrative across themes to being integrative across multiple disciplines, requiring the expertise of and tuning between different actors and expertise domains. Our aim is to stimulate debate in the field and set out a renewed research agenda by means of calling for additional considerations of strategic aspects, namely technology, people, and ethics, that can help guide and move the field forward.

The problem of assessment of Decision Making Units (DMUs) by using Data Envelopment Analysis (DEA) may not be straightforward due to the data uncertainty. Several studies have been developed to incorporate uncertainty into input/output values in the DEA literature. On the other hand, while traditional DEA models focus more on crisp data, there exist many applications in which data is reported in form of intervals. This paper considers the box-uncertainty in data which means that each input/output value is selected from a symmetric box. This specific type of uncertainty has been addressed as Interval DEA approaches. Our proposed model deals with efficiency evaluation of DMUs with imprecise data in a robust optimization. We assume that inputs and outputs are reported in the form of intervals and propose the robust counterpart problem for the envelopment form of the DEA model. Further, we also develop two ranking methods which have more benefits compared to some existing approaches. An illustrative example is provided to show how the proposed approaches work. An application on hospital efficiency in East Virginia is used to show the usefulness of the proposed approaches.

Allocating a fixed cost among a set of peer decision-making units (DMUs) is one of the most important applications of data envelopment analysis. However, almost all existing studies have addressed the fixed cost allocation (FCA) problem within a traditional framework while ignoring the existence of undesirable outputs. Undesirable outputs are neither scarce in various production activities in real world applications nor trivial in efficiency evaluation and subsequent decision making. Motivated by this observation, this article attempts to explicitly extend the traditional FCA problem to situations in which DMUs are necessarily involved with undesirable outputs. To this end, we first investigate the efficiency evaluation of DMUs considering undesirable outputs based on the joint weak disposability assumption. Then, flexible FCA schemes are considered to revisit the efficiency evaluation process. The results show that feasible allocation schemes exist such that all DMUs can be simultaneously efficient. Furthermore, we define the comprehensive satisfaction degree and develop a satisfaction degree bargaining game approach to determine a unique FCA scheme. Finally, the proposed approach is tested with an empirical study of banking activities based on real conditions.

Data envelopment analysis (DEA) is one of the widely used methods to measure the efficiency scores of decision making units (DMUs). Conventional DEA is unable to consider both uncertainty in data and decision makers' (DMs) judgments in the evaluations. This study, to address the shortcomings of the conventional DEA, proposes a new best worst method (BWM)- robust credibility DEA (BWM-RCDEA) model to estimate the efficiency scores of DMUs considering DMs' preferences and uncertain data, simultaneously. First, to handle uncertainty in input and output variables, fuzzy credibility model has been applied. Additionally, uncertainty in constructing fuzzy sets is modeled using robust optimization with fuzzy perturbation degree. In this paper, two new types of RCDEA models are proposed: RCDEA model with exact perturbation in fuzzy inputs and outputs and RCDEA model with fuzzy perturbation in fuzzy inputs and outputs. In addition, to deal with flexibility of weights and incorporating DMs' judgement into the RCDEA model, a bi-objective BWM-RCDEA model is introduced. Finally, the proposed bi-objective model is solved using min-max approach. To illustrate the usefulness and capability of the proposed model, efficiency scores of 39 distribution companies in Iran is investigated and results are analyzed and discussed. Finally, based on the results, recommendations have been made for policy makers.

The significant positive and negatives effects of transportation systems (TSs) on the sustainability of cities and human life draw much attention from both researchers and managers. Constructing bus rapid transit (BRT) networks, or adding new lines to the existing ones, is one of the cheapest and easiest solutions to improve the performance of the urban transportation network (UTN). Often, large number of candidate projects (BRT lines) renders the execution of all these projects impossible due to technical and financial limitations. Hence, evaluating the candidate projects and developing the best plan for constructing a BRT network is an important issue requiring a complex decision-making process. In this study, a multi-period triple-level sustainable BRT network design model has been proposed using data envelopment analysis, game theory, Malmquist Index (MI) and considering all sustainability dimensions including environment, economic and society. Both managers' and passengers' perspectives have been considered in the modeling. A procedure based on a genetic algorithm (GA) has been developed to solve the presented triple-level model. Finally, the model has been applied to a real-world case study of evaluating and selecting the BRT projects in the city of Isfahan, and the results have been analyzed.

Conventional DEA performs like a " black box " and provides no information about sub-processes. In some cases, such as banks, providing services is made up of interactive and interdependent processes. Also, in real world applications, inputs could be shared among these sub-processes. Moreover, due to the characteristics of some variables, such as number of employees, only integer values could be assigned to them. Hence, to address these shortcomings, in this study, a mixed integer network DEA (MI-NDEA) with shared inputs and undesirable outputs has been proposed to evaluate the efficiency of decision making units. The proposed model considers integer values for some of the input variables. Also, it assumes that some inputs are shared among different stages of the production process. To illustrate the capability of the model, the efficiency of " Internet banking " , " profitability " , " production " and " overall " performance of a set of bank branches have been evaluated and results are discussed. The results indicate that the mean of overall efficiency for all branches is high. However, some branches are not efficient enough in the " Production " stage or " Profitability " stage. To identify the source of inefficiency in such branches, projection values have been calculated and recommendations have been made for policy makers.

Performance. RAIRO Operations Research, 56 (2): 911-930. https://doi. Abstract Data envelopment analysis (DEA) model has been widely applied for estimating efficiency scores of decision making units (DMUs) and is especially used in many applications in transportation. In this paper, a novel common weight credibility DEA (CWCDEA) model is proposed to evaluate DMUs considering uncertain inputs and outputs. To develop a credibility DEA model, a credibility counterpart constraint is suggested for each constraint of DEA model. Then, the weights generated by the credibility DEA (CDEA) model are considered as ideal solution in a multi-objective DEA model. To solve the multi-objective DEA model, a goal programming model is proposed. The goal programming model minimized deviations from the ideal solutions and found the common weights of inputs and outputs. Using the common weights generated by goal programming model, the final efficiency scores for decision making are calculated. The usefulness and applicability of the proposed approach have been shown using a data set in the airline industry.

Almost a decade ago, the data scientist job was named the sexiest job of the 21st century by Harvard Business Review[1]. Today, this assertion still holds. One of the most fascinating aspects is that there is no singlecareer path to becoming a data scientist. Data scientists can emerge from virtually any field, from computer science to linguistics, because data science is simply such a vast domain that builds upon… well… so many other domains.

(2022). Environmental efficiency under weak disposability: An improved super efficiency data envelopment analysis model with application for assessment of ports operations. Environment, Development and Sustainability, https://doi. ABSTRACT Due to ports have rapidly been expanding, air pollution resulted from port operations has expansion and become a persistent concern for environmentalist and policy makers. The objective of this paper is to measure the environmental efficiency of ports in Korea. The main characteristic of the environmental efficiency assessment problem is that undesirable output of carbon dioxide (CO2) emissions and exogenous fixed input of the terminal area of ports should concurrently be considered. By analysing the impacts of the exogenous fixed input and undesirable outputs on decision making units (DMUs) performance, a super efficiency slacks-based measure in data envelopment analysis (SE-SBM-DEA) approach is proposed. The proposed approach consists of two models are slacks-based measure (SBM) model and super efficiency SBM (SE-SBM) model. The models effectively discriminate between efficient and inefficient ports, and rank their efficiencies. To restrict any decreases or increases in the fixed input levels, the slacks of fixed inputs are removed from the target functions and their relevant constraints of the proposed approach. In addition, the undesirable outputs are formulated according to the weak disposability assumption, so that they can only be reduced with the reduction of certain desirable outputs. Hence, its slack should also be removed from the SBM and SE-SBM models. As a result, the scalar measures of the models only deal with the discretionary inputs and desirable outputs of a DMU being evaluated. We examine the applicability of the proposed approach, using real data, for 19 ports in Korea.

Uncertainty is an important issue to consider when evaluating entities in both public and private sectors. On the other hand, many operations have more than one stage process when some inputs are fed to the system to produce a number of intermediate measures. The intermediate measures are then transformed into final products in the subsequent stages. The composition method in network data envelopment analysis (NDEA) is a popular method for measuring the efficiency of a two-stage process. The composition method is fractional bi-objective programming that is solved by non-linear programming techniques such as bisection search. In this paper, the two-stage NDEA is extended with negative data and undesirable outputs. First, we propose an alternative linear model based on the goal programming technique to avoid complex non-linear calculations. Then, we use a method to transform negative data into positive and undesirable outputs into desirable ones. Finally, we develop the proposed model using the fuzzy α-cut approach in order to incorporate data uncertainty in the linear goal programming (GP) model. To validate the accuracy of the proposed model, a numerical example is solved. To show the applicability of the proposed model, a real case of 22 insurance companies is examined. We also perform a comparative analysis to specify the benchmark and inefficient companies. Comparative analysis can help managers to recognize where improvement should be investigated with priority.

Performance evaluation enables decision makers (DMs) to have a better view about the weaknesses and strengths of leading units to improve efficiencies as a crucial goal. Data envelopment analysis (DEA) is the most popular technique to measure performance efficiency of decision making units (DMUs). However, conventional DEA is unable to consider uncertainty of input and output data in the evaluations. In this study, in order to address uncertainty in data, a robust credibility DEA (RCDEA) model has been introduced. First, a fuzzy credibility approach is used to construct fuzzy data. Then, a robust optimization approach is applied to consider uncertainty in constructing fuzzy sets. Moreover, perturbation level is considered as exact and fuzzy values. To illustrate the capability of the proposed model, 28 hospitals are evaluated in northwestern region of Iran and results are analyzed. According to the results, as perturbation degree increases, DMUs get normalized lower efficiencies and vise-versa.

This paper evaluates the stock performance of Islamic banks relative to their conventional counterparts during the initial phase of the COVID-19 crisis (from December 31, 2019, to March 31, 2020). Using 426 banks from 48 countries, we find that stock returns of Islamic banks were about 10-13% higher than those of conventional banks after controlling for a host of the bank- and country-level variables. This study explains the Islamic banks' superior crisis stock performance by exploring the potential role of pre-crisis bank efficiency. In a univariate analysis, we document higher non-parametric Data Envelopment Analysis (DEA) efficiency levels for Islamic banks than conventional banks in the year preceding the COVID-19 crisis. Our multivariate regressions show that the risk-adjusted DEA efficiency scores can explain crisis stock returns for Islamic banks but not conventional banks. The evidence is robust to alternative measures of stock returns, efficiency models, and other empirical strategies. Finally, we present insight on the importance of key bank characteristics in determining the stock returns of conventional banks during the crisis period.

As risks of all sorts, from economic and financial crises to terrorism acts and pandemics, keep on characterising and affecting all aspects of life globally, at the individual and societal level, national and international organisations, as well as governments, need to be constantly adapting and collaborating through international diplomacy to pursue common goals for people’s well-being. This is where the topic of composite indices comes up. Composite indices are used by national and international organisations, and governments and businesses alike, to monitor different performance aspects of the economy of a country and the people therein; and they have historically been valuable as communication tools and as inputs into decision and policymaking. In this work, we delve into the relevant literature to explore the link between international diplomacy, institutions, and composite indices, with the aim to highlight the usefulness of composite indices in practice. We conclude with final thoughts and recommendations for future research on the topic.

Sustainable development has gained significant attention in the literature due to the increased global awareness of environmental sustainability during the last decade. Sustainable development has three aspects, including economic, social, and environmental. The challenge of sustainable development is to establish a balance between these three aspects. Assessing the efficiency of a company contributes comprehensive information to improve its overall performance. Despite numerous studies in this field, the literature lacks studies that simultaneously consider all three aspects of sustainable development, especially the social aspect. The main objective of this paper is to calculate the technical, social, and environmental efficiency scores. We also introduce a new efficiency called sustainable efficiency that merges all three sustainable development aspects in one efficiency score. This study applies two existing data envelopment analysis (DEA) models to evaluate technical, social, environmental, and sustainable efficiencies. These models, namely the three-step method and the modified three-step method, are computationally intensive. Also, this paper introduces two new DEA models, namely the common weight goal programming DEA and the common weight DEA, to assess the efficiencies with much fewer computations. Each model produces results that are different from one another. Therefore, the TOPSIS approach is applied to provide an overall result by integrating the results obtained from the four presented models. For this purpose, the implementation of four TOPSIS models is required. To illustrate the capability and validity of the developed models in efficiency calculation, a case of Iranian airlines is presented. The selected airlines are evaluated in different aspects and final results are obtained by applying TOPSIS. The findings show that using TOPSIS to combine the results of several DEA models lead to fully ranking of airlines in four aspects of technical, social, environmental, and sustainable efficiencies. Also, it is recommended to managers to probe pairwise comparison between different efficiencies of airlines in order to find and improve the weak ones.

This book aims to provide the necessary background to work with big data blockchain by introducing some novel applications in service operations for both academics and interested practitioners, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book intends to cover theory, research, development, and applications of big data and blockchain, as embedded in the fields of mathematics, engineering, computer science, physics, economics, business, management, and life sciences, to help service operations management.

The Industry 4.0 (I4.0) revolution has led to rapid digital transformation, automation of manufacturing processes and efficient decision-making in business operations. Despite the potential benefits of I4.0 technologies in operations management reported in the extant literature, there has been a paucity of empirical research examining the intention to adopt I4.0 technologies for managing risks. Risk management identifies, assesses, and introduces responses for risks to avert crises. This study combines institutional theory, the resource-based view and the technology acceptance model to develop a novel behavioural model examining the adoption of big data, artificial intelligence, cloud computing, and blockchain for risk management from the operations manager's perspective, which has never been examined in the literature. The model was tested for each I4.0 technology using data collected from 117 operations managers in the UK manufacturing industry which were analysed using structural equation modelling. We contribute to the theory on I4.0 in digital manufacturing by showing the impact of digital transformation maturity, market pressure, regulations, and resilience on the perceived usefulness and adoption of these technologies for managing risks in business operations. Based on the findings, we discuss implications for operations managers effectively and efficiently to adopt I4.0 technologies aiming to boost operational productivity.

This study surveys the ordered weighted averaging (OWA) operator literature using a citation network analysis. The main goals are the historical reconstruction of scientific development of the OWA field, the identification of the dominant direction of knowledge accumulation that emerged since the publication of the first OWA paper, and to discover the most active lines of research. The results suggest, as expected, that Yager's paper (IEEE Trans. Systems Man Cybernet, 18(1), 183-190, 1988) is the most influential paper and the starting point of all other research using OWA. Starting from his contribution, other lines of research developed and we describe them.

Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim of this paper is to propose an approach for performance evaluation of mutual fund by considering internal structure and financial data uncertainty. To reach this goal, the robust network data envelopment analysis (RNDEA) is presented for extended two-stage structure. In the RNDEA method, leader-follower (non-cooperative game) and robust optimization approaches are applied in order to modeling network data envelopment analysis (NDEA) and dealing with uncertainty, respectively. The proposed RNDEA approach is implemented for performance assessment of 15 mutual funds. Illustrative results show that presented method is applicable and effective for performance evaluation and ranking of MFs in the presence of uncertain data. Also, the results reveal that the discriminatory power of robust NDEA approach is more than the discriminatory power of deterministic NDEA models.

Determining energy productivity change during a time interval is an important issue in many production lines. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to measure productivity change and widely used by researchers to analyze the performance of decision making units. In this regard, the modified Enhanced Russell Measure (ERM), a non-radial DEA-based efficiency model, is applied to develop new models for measuring the Malmquist productivity index (MPI). To present productivity changes of decision making units (DMUs) over time more truly and more comprehensively than the conventional MPI method, this paper proposed three new approaches by using optimistic, pessimistic, and general viewpoints of data envelopment analysis. However, in many production processes, undesirable outputs such as smoke or waste pollution may be generated. Thus, this paper has further developed the proposed approaches in the presence of an undesirable output. The proposed methodology is applied to evaluate the productivity changes and efficiencies of chickpea production farms in 16 provinces in Iran.