Professor Ali Emrouznejad

Professor and Chair in Business Analytics, BSc, MSc, PGc, PhD, FORS, FIMA
Director, Centre for Business Analytics in Practice


Areas of specialism

Big Data & AI; Data Envelopment Analysis; Data Mining; Performance Measurement and Performance Management; Net zero and Sustainability Analytics


Ali Emrouznejad, Vishal Panchmatia, R. Gholami, Carolee Rigsbee, Hasan Kartal (2023)Analysis of Smart Meter Data With Machine Learning for Implications Targeted Towards Residents, In: International journal of urban planning and smart cities4(1)pp. 1-22 IGI Glogal

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.

Ali Emrouznejad, Gholam R. Amin, Mojtaba Ghiyasi, Maria Michali (2023)A Review of Inverse Data Envelopment Analysis: Origins, Development, and Future Directions, In: IMA journal of management mathematicsdpad006 Oxford University Press

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 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.

Ali Emrouznejad, Gholam R. Amin (2023)Advances in Inverse Data Envelopment Analysis: Empowering Performance Assessment, In: IMA Journal of Management Mathematicsdpad004 Oxford University Press

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

N. Boustani, A. Emrouznejad, R. Gholami, O. Despic, A. Ioannou (2023)Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks, In: Annals of Operations ResearchIn press(In press) Springer
Ali Emrouznejad, Marianna Marra, Guo-liang Yang, Maria Michali (2023)Eco-efficiency considering NetZero and Data Envelopment Analysis: A critical literature review, In: IMA journal of management mathematicsdpad002 Oxford University Press

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.

Hashem Omrani, Arash Alizadeh, Ali Emrouznejad, Tamara Teplova (2022)A robust credibility DEA model with fuzzy perturbation degree: An application to hospitals performance, In: Expert Systems with Applications189116021 Elsevier

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.

Vincent Charles, Ali Emrouznejad, Tatiana Gherman (2022)Strategy Formulation and Service Operations in the Big Data Age: The Essentialness of Technology, People, and Ethics, In: Big Data and Blockchain for Service Operations Managementpp. 19-48 Springer International Publishing

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.

Pejman Peykani, Ali Emrouznejad, Emran Mohammadi, Jafar Gheidar-Kheljani (2022)A Novel Robust Network Data Envelopment Analysis Approach for Performance Assessment of Mutual Funds under Uncertainty, In: Annals of Operations Research Springer

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.

Hashem Omrani, Arash Alizadeh, Ali Emrouznejad, Zeynab Oveysi (2022)A Novel Best-Worst-Method two-stage DEA model considering decision makers' preferences: An application in bank branches evaluation, In: International Journal of Finance and Economics Wiley

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.

Vincent Charles, Ali Emrouznejad (2022)Modern Indices for International Economic Diplomacy Springer International Publishing

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.

Reza Mahmoudi, Seyyed-Nader Shetab-Boushehri, Ali Emrouznejad (2021)Sustainability in the evaluation of bus rapid transportation projects considering both managers and passengers perspectives: A triple-level efficiency evaluation approach, In: International Journal of Sustainable Transportationpp. 1-19 Taylor & Francis

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.

Hashem Omrani, Zeynab Oveysi, Ali Emrouznejad, Tamara Teplova (2022)A Mixed Integer Network DEA with Shared Inputs and Undesirable Outputs for Performance Evaluation: Efficiency Measurement of Bank Branches, In: The journal of the Operational Research Society Routledge

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.

Amar Oukil, Ali Emrouznejad, Ahmed El-Bouri (2022)Energy-aware job scheduling in a multi-objective production environment – An integrated DEA-OWA model, In: Computers & Industrial Engineering168108065 Elsevier

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.

Hashem Omrani, Ali Emrouznejad, Meisam Shamsi, Pegah Fahimi (2022)Evaluation of Insurance Companies Considering Uncertainty: A Multi- Objective Network Data Envelopment Analysis Model with Negative Data and Undesirable Outputs, In: Socio-economic planning sciences101306 Elsevier

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.

Mushtaq Taleb, Ruzelan Khalid, Ali Emrouznejad, Razamin Ramli (2022)Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero, In: Environment, Development and Sustainability Springer

(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.

Mohammad Zarei Mahmoudabadi, Ali Emrouznejad (2022)Balanced Performance Assessment under uncertainty: An Integrated CSW-DEA and Balanced Scorecard (BSC), In: Annals of Operations Research Springer Nature

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.

Vincent Charles, Tatiana Gherman, Ali Emrouznejad (2022)Characteristics and Trends in Big Data for Service Operations Management Research: A Blend of Descriptive Statistics and Bibliometric Analysis, In: Big Data and Blockchain for Service Operations Managementpp. 1-18 Springer International Publishing

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.

Majid Azadi, Reza Kazemi Matin, Ali Emrouznejad, William Ho (2022)Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero, In: Annals of operations research [electronic resource] Springer

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.

Ali Emrouznejad, Patanjal Kumar, Sachin Kumar Mangla, Yigit Kazancoglu (2022)A decision framework for incorporating the coordination and behavioural issues in sustainable supply chains in digital economy, In: Annals of Operations Research

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.

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.

Ali Emrouznejad, Guo-liang Yang, Mohammad Khoveyni, Maria Michali, Maria Michali (2022)Data Envelopment Analysis: Recent developments and challenges, In: The Palgrave Handbook of Operations Research pp. 307-350 Palgrave Macmillan

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.

Ali Mirzaei, Mohsen Saad, Ali Emrouznejad (2022)Bank stock performance during the COVID-19 crisis: does efficiency explain why Islamic banks fared relatively better?, In: Annals of Operations Research Springer Nature

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.

Maria Michali, Ali Emrouznejad, Akram Dehnokhalaji, Ben Clegg (2023)Subsampling bootstrap in network DEA, In: European Journal of Operational Research305(2)pp. 766-780 Elsevier

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.

Konstantinos Petridis, N Petridis, Ali Emrouznejad, Fouad Ben Abdelaziz (2021)Prioritizing of volatility models: a computational analysis using data envelopment analysis, In: International transactions in operational research Wiley

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.

Akram Dehnokhalaji, Somayeh Khezri, Ali Emrouznejad (2022)A box-uncertainty in DEA: A robust performance measurement framework, In: Expert Systems with Applications187115855 Elsevier

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.

Hashem Omrani, Arash Alizadeh, Ali Emrouznejad, Tamara Teplova (2021)Data envelopment analysis model with decision makers' preferences: a robust credibility approach, In: Annals of Operations Research Springer

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.

Reza Mahmoudi, Ali Emrouznejad (2022)A multi-period performance analysis of airlines: A game-SBM-NDEA and Malmquist Index approach, In: Research in Transportation Business and Management100801 Elsevier

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.

Amir Moradi-Motlagh, Ali Emrouznejad (2022)The origins and development of statistical approaches in non-parametric frontier models: A survey of the first two decades of scholarly literature (1998-2020), In: Annals of Operations Research318pp. 713-741 Springer Nature

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.

Alireza Khoshroo, Mommad Izadikhah, Ali Emrouznejad (2022)Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index, In: Energy258124819 Elsevier

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.

Hashem Omrani, Meisam Shamsi, Ali Emrouznejad (2022)Evaluating Sustainable Efficiency of Decision Making Units Considering Undesirable Outputs: An Application to Airline Using Integrated Multi-Objective DEA-TOPSIS, In: Environment, Development and Sustainability Springer

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.

FF Li, Yue Wang, Ali Emrouznejad, Q-X Zhu, Gang Kou (2021)Allocating a fixed cost across decision-making units with undesirable outputs: A bargaining game approach, In: The Journal of the Operational Research Society Taylor and Francis

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.

Mohamad Reza Pakravan-Charvadeh, Cornelia Butler Flora, Ali Emrouznejad (2022)Impact of Socio-Economic Factors on Nutrition Efficiency: An Application of Data Envelopment Analysis, In: Frontiers in Nutrition9859789

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.

O. Rodriguez-Espindola, S Chowdhury, Prasanta Kumar Dey, Pavel Albores, Ali Emrouznejad (2022)Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing, In: Technological Forecasting and Social Change178121562 Elsevier

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.

Vincent Charles, Tatiana Gherman, Ali Emrouznejad (2022)The Role of Composite Indices in International Economic Diplomacy, In: Modern Indices for International Economic Diplomacypp. 1-17 Springer International Publishing

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.

Majid Azadi, Ali Emrouznejad, Fahimeh Ramezani, Farookh Khadeer Hussain (2022)Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis, In: IEEE Transactions on Cloud Computing10(1)pp. 348-355 Institute of Electrical and Electronics Engineers (IEEE)

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.

Ali Emrouznejad, Vincent Charles (2022)Big Data and Blockchain for Service Operations Management Springer Nature Switzerland

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.

Josef Jablonsky, Ali Emrouznejad, Mehdi Toloo (2018)Editorial: Special issue on data envelopment analysis, In: Central European journal of operations research26(4)pp. 809-812 Springer Nature
Mehdi Toloo, Ali Emrouznejad, Placido Moreno (2017)A linear relational DEA model to evaluate two-stage processes with shared inputs, In: Computational and Applied Mathematics36(1)pp. 45-61 Springer

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.

Behrouz Arabi, Susila Munisamy, Ali Emrouznejad, Mehdi Toloo, Mohammad Sadegh Ghazizadeh (2016)Eco-efficiency considering the issue of heterogeneity among power plants, In: Energy111pp. 722-735 Elsevier

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.

Mehdi Toloo, Ameneh Zandi, Ali Emrouznejad (2015)Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach, In: Journal of Supercomputing71(7)pp. 2397-2411 Springer

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.

Vincent Charles, Ali Emrouznejad, Tatiana Gherman (2022)Two types of stories that data scientists can tell, In: Inside OR(614)pp. 16-17

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.

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