The catchment area along a bus route is key in predicting bus journeys. In particular, the aggregated number of households within the catchment area are used in the prediction model. The model uses other factors, such as head-way, day-of-week and others. The focus of this study was to classify types of catchment areas and analyse the impact of varying their sizes on the quality of predicting the number of bus passengers. Machine Learning techniques: Random Forest, Neural Networks and C5.0 Decision Trees, were compared regarding solution quality of predictions. The study discusses the sensitivity of catchment area size variations. Bus routes in the county Surrey in the United Kingdom were used to test the quality of the methods. The findings show that the quality of predicting bus journeys depends on the size of the catchment area.
Pharmacovigilance has attracted enormous attention over recent decades. At present, the increasing datafication combined with the growing knowledge elicitation capabilities of key technology innovations, present pharmacovigilance with enormous opportunities to improve its effectiveness and widen its scope. With change being a continuous process, for pharmacovigilance this represents an era of “Digital Darwinism”, during which new directions are opening fast and new challenges emerge, as to how the sector adapts in order to draw benefit. Current efforts and initiatives, aimed at addressing existing barriers and at enhancing the practical applications of new science and technology, are fragmented and disjoint, and thus are not adequate to provide an effective response to challenges. This research proposes a new paradigm for collaborative, information-driven innovation in pharmacovigilance and develops a Reference Framework, in order to (a) deepen the collective understanding of how a principled, collaborative and balanced medicines safety data ecosystem can be organised, (b) guide stakeholders towards the optimisation of pharmacovigilance and (c) provide useful reference points for the ongoing research and development process in the field. The Knowledge Discovery Cube (KDC) Framework provides the means for continual analysis, and for managing technology adoption in an informed and intentional manner. A variety of sources informed the research work. The resulting deliberations draw on the findings and conclusions of scholarly research, guidelines, policy documents and reports, and other resources from within and outside the field of health and life sciences, as well as on relevant theories. The developed framework was operationalised and validated in the context of vaccine safety monitoring.
This action research thesis was conducted at a Philippine based entity of a multinational semiconductor corporation. It was motivated by a strong interest in developing a method to increase the human component in purchasing efficacy to facilitate the integration of the purchasing function with other areas of the corporation as well as to gain strategic influence in this business-critical area. Recent research findings have reinforced the importance of uncovering the full strategic potential of the purchasing function within corporations. Literature widely acknowledges that the degree of purchasing efficacy to drive integration is seen as a major success factor in establishing cross functional alignment between the purchasing functions and their related interfaces. This in turn represents the internal dimension of purchasing strategy. Consequently, there is an ongoing practical need, as well as strong research interest, to provide a tool to assist in this integration process. Transformative learning theory argues that learning mainly occurs at the moment when meaning perspectives and meaning schemes change. It further postulates that transformation of meaning is initiated by reinterpreting past experiences and reframing them based on new experiences. Such a learning process is often activated by a “distorting dilemma” where previous meanings are suddenly no longer valid. The theory of constraints provides a framework for problem solving which includes methods to facilitate logical thinking processes and contains a tool named the current reality tree. This tool facilitates interpreting reality through a guided and rules based reasoning process which is based on methods of validity testing to prove assumptions of reality. This thesis is concerned with the increase of purchasing efficacy through a transformative learning process facilitated by the application of the logical thinking processes. Ultimately, the study presents a methodology to increase purchasing efficacy by transforming mind-sets and to increase the level of consciousness by utilizing a logical thinking process framework. However, some limitations, such as a regional and cultural focus on the Philippines, as well as the specific context in which this research was carried out, should be considered. These shortcomings are mentioned to motivate scholars to enter future research avenues in purchasing and supply management, in organizational change processes and in the application of the logical thinking process framework.
Since the bus deregulation (Transport Act 1985) the patronage for bus services has been decreasing in a county in South of England. Hence, methods that increase patronage, focus subsidies and stimulate the bus industry are required. Our surveys and market research identified and quantified essential factors. The top three factors are price, frequency, and dependability. The model was further enhanced by taking into account real time passenger information (RTPI), socio-demographics and ticket machine data along targeted bus routes. These allowed the design of predictive models. Here, feature engineering was essential to boost the solution quality. We compared several models such as regression, decision tress and random forest. Additionally, traditional price elasticity formulas have been confirmed. Our results indicate that more accuracy can be gained using prediction methods based on the engineered features. This allows to identify routes that have the potential to increase in profitability - allowing a more focused subsidy strategy.
Scheduling multiple products with limited resources and varying demands remain a critical challenge formany industries. This work presents mixed integer programs(MIPs) that solve the Economic Lot SizingProblem (ELSP) and other Dynamic Lot-Sizing (DLS) models with multiple items. DLS systems are clas-sified, extended and formulated as MIPs. Especially, logical constraints are a key ingredient in succeedingin this endeavour. They were used to formulate the setup/changeover of items in the production line. Min-imising the holding, shortage and setup costs is the primaryobjective for ELSPs. This is achieved by findingan optimal production schedule taking into account the limited manufacturing capacity. Case studies for aproduction plants are used to demonstrate the functionality of the MIPs. Optimal DLS and ELSP solutionsare given for a set of test-instances. Insights into the runtime and solution quality are given.
Abstract The mTSP is solved using an exact method and two heuristics, that balances the number of nodes per route. The first heuristic uses a nearest node approach and the second assigns the closest salesman. A comparison of heuristics with test-instances being in the Euclidean plane showed that the closest node approach delivers better solutions and a faster runtime. On average, the closest node solutions are approximately one percent better than the other heuristic. Furthermore, it is found that increasing the number of salesman or customers results in a distance growth for uniformly distributed nodes in an Euclidean grid plane. The distance growth is almost proportional to the square root of number of customers (nodes). In this context we reviewed the expected distance of two uniformly distributed random (real and integer) points. The minimum distance of a node to n uniformly distributed random (real and integer) points was derived and expressed as functional relationship. This gives theoretical underpinnings for - previously - empirical distance to salesmen growth insights.
Lean and Swift-Even-Flow (SEF) operations are compared in the context of sorting facilities. Lean approaches tend to attack parts of their processes for improvement and waste reduction, sometimes overlooking the impact this will have on their overall pipeline. A SEF approach on the other hand is driven by a desire to reduce variations by enabling the practitioner to visualise himself as the material that flows through the system thus unearthing all the problems that occur in the process as a whole. This study integrates Discrete Event Simulations (DES) into the lean and SEF framework. A real world case study with high levels of variations is used to gain insights and to derive relevant simulation models. The models were used to find the optimal configuration of machines and labour such that the operational costs are minimised. It was found that DES and SEF have a common basis. Lean processes as well as SEF processes both converge to similar solutions. However, SEF arrives faster at a near optimum solution. DES is a valuable tool to model, support and implement the lean and SEF approach. The SEF approach is superior to lean processes in the initial phases of a business process optimisation. The primary novelty of this study is the usage of DES to compare the lean and SEF approach. This study presents a systematic approach of how DES and optimisation can be applied to lean and SEF operations.
Industrial practices and experiences highlight that demand is dynamic and non-stationary. Research however has historically taken the perspective that stochastic demand is stationary therefore limiting its impact for practitioners. Manufacturers require schedules for multiple products that decide the quantity to be produced over a required time span. This work investigated the challenges for production in the framework of a single manufacturing line with multiple products and varying demand. The nature of varying demand of numerous products lends itself naturally to an agile manufacturing approach. We propose a new algorithm that iteratively refines production windows and adds products. This algorithm controls parallel genetic algorithms (pGA) that find production schedules whilst minimizing costs. The configuration of such a pGA was essential in influencing the quality of results. In particular providing initial solutions was an important factor. Two novel methods are proposed that generate initial solutions by transforming a production schedule into one with refined production windows. The first method is called factorial generation and the second one fractional generation method. A case study compares the two methods and shows that the factorial method outperforms the fractional one in terms of costs.
In this paper a demand time series is analysed to support Make-To-Stock (MTS) and Make-To-Order (MTO) production decisions. Using a purely MTS production strategy based on the given demand can lead to unnecessarily high inventory levels thus it is necessary to identify likely MTO episodes. This research proposes a novel outlier detection algorithm based on special density measures. We divide the time series' histogram into three clusters. One with frequent-low volume covers MTS items whilst a second accounts for high volumes which is dedicated to MTO items. The third cluster resides between the previous two with its elements being assigned to either the MTO or MTS class. The algorithm can be applied to a variety of time series such as stationary and non-stationary ones. We use empirical data from manufacturing to study the extent of inventory savings. The percentage of MTO items is reflected in the inventory savings which were shown to be an average of 18.1%.
Decision making is often based on Bayesian networks. The building blocks for Bayesian networks are its conditional probability tables (CPTs). These tables are obtained by parameter estimation methods, or they are elicited from subject matter experts (SME). Some of these knowledge representations are insufficient approximations. Using knowledge fusion of cause and effect observations lead to better predictive decisions. We propose three new methods to generate CPTs, which even work when only soft evidence is provided. The first two are novel ways of mapping conditional expectations to the probability space. The third is a column extraction method, which obtains CPTs from nonlinear functions such as the multinomial logistic regression. Case studies on military effects and burnt forest desertification have demonstrated that so derived CPTs have highly reliable predictive power, including superiority over the CPTs obtained from SMEs. In this context, new quality measures for determining the goodness of a CPT and for comparing CPTs with each other have been introduced. The predictive power and enhanced reliability of decision making based on the novel CPT generation methods presented in this paper have been confirmed and validated within the context of the case studies.
In this paper we study the optimality of production schedules in the food industry. Specifically we are interested whether stochastic economic lot scheduling based on aggregated forecasts outperforms other lot sizing approaches. Empirical data on the operation’s customer side such as product variety, demand and inventory is used. Hybrid demand profiles are split into make-to-order (MTO) and make-to-stock (MTS) time series. We find that the MTS demand aggregation stabilizes, minimizes change-overs, and optimizes manufacturing.
This book explores a set of critical areas of Operations Management in depth. The contents covers topics such as: - Waiting Line Models (e.g. Multiple Server Waiting Line) - Transportation and Network Models (e.g. Shortest Route) - Inventory Management (e.g. Economic Order Quantity model) This will enable the reader: - To increase the efficiency and productivity of business firms - To observe and define “challenges” in a concise, precise and logical manner - To be familiar with a selected number of classical and state-of-the art Management Science methods and tools to solve management problems - To create solution models, to develop and create procedures that offer competitive advantage to the business/organisation - To communicate and provide results to the management for decision making and implementation
Existing works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems (CAS) or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity, but without providing a thorough discussion about when and why they should be deployed. This research seeks to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity. This research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. We illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer. Regardless of its type or origin, supply chain complexity can arise due to the strategic business requirements of the BU (strategic) or due to suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that efforts to address supply chain complexity can reveal other system weaknesses that lie dormant until the system is stressed. The case study provides empirical support for the literature-derived conceptual model. Nevertheless, any findings derived from a single, in-depth case study require further research to produce generalizable results. The conceptual model presented here provides a more granular view of supply chain complexity, and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response. We are aware of no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual business unit (BU) should respond to supply chain complexity. As such, this paper furthers our understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.
In this paper we study the profitability of car manufacturers in relation to industry-wide profitability targets such as industry averages. Specifically we are interested in whether firms adjust their profitability in the direction of these targets, whether it is possible to detect any such change, and, if so, what the precise nature is of these changes. This paper introduces several novel methods to assess the trajectory of profitability over time. In doing so we make two contributions to the current body of knowledge regarding the dynamics of profitability. First, we develop a method to identify multiple profitability targets. We define these targets in addition to the commonly used industry average target. Second, we develop new methods to express movements in the profitability space from t to t + j, and define a notion of agreement between one movement and another. We use empirical data from the car industry to study the extent to which actual movements are in alignment with these targets. Here we calculate the three targets that we have previously identified, and contrast them with the actual profitability movements using our new agreement measure. We find that firms tend to move more towards to the new targets we have identified than to the common industry average. © 2012 Elsevier B.V. All rights reserved.
In the United Kingdom, local councils and housing associations provide social housing at secure, low-rent housing options to those most in need. Occasionally tenants have difficulties in paying their rent on time and fall into arrears. The lost revenue can cause financial burden and stress to tenants. An efficient arrear management scheme is to target those who are more at risk of falling into long-term arrears so that interventions can avoid lost revenue. In our research, a Long Short-Term Memory Network (LSTM) based time series prediction model is implemented to differentiate the high-risk tenants from temporary ones. The model measures the arrear risk for each individual tenant and differentiates between short-term and long-term arrears risk. Furthermore it predicts the trajectory of arrears for each individual tenant. The arrears analysis investigates factors that provide assistance to tenants to trigger preventions before their debt becomes unmanageable. A five-years rent arrears dataset is used to train and evaluate the proposed model. The root mean squared error (RMSE) punishes large errors by measuring differences between actually observed and predicted arrears. The novel model benefits the sector by allowing a decrease in lost revenue; an increase in efficiency; and protects tenants from unmanageable debt.
This research examines the application of the Theory of Swift, Even Flow (TSEF) by a distribution company to improve the performance of its processes for parcels. TSEF was deployed by the company after experiencing improvement fatigue and diminishing returns from the time and effort invested. The fatigue was resolved through the deployment of swift, even flow and the adoption of "focused factories". The case study conducted semi-structured interviews, mapped the parcel processes and applied Discrete Event Simulation (DES). From this study we not only documented the value of TSEF as a strategic tool but we also developed insights into the challenges that the firm encountered when utilising the concept. DES confirmed the feasibility of change and its cost savings. This research demonstrates DES as tool for TSEF to stimulate management thinking about productivity
Drone delivery services (DDS) are an upcoming reality. Companies such as Amazon, DHL and Google are investing in developments in this area. German’s parcel delivery company DHL has begun applying them commercially. This emphasises the importance to have insights in the economic benefits of drone delivery services. This study compares drone delivery services with traditional delivery services. It looks at the cost effectiveness of integrating drones into a delivery services’ operations. The study identifies and categorise factors that control the delivery operations. It develops a mathematical model that compares 3D flight paths of a fleet of drones with a fleet of 2D van routes. The developed method can be seen as an extension of the Vehicle Routing Problem (VRP). Efficiency savings of drone services of real world rural postcode sectors are analysed. The study limits itself to the use of small unmanned aerial vehicles (UAVs) with a low payload, which are GPS controlled and autonomous. The case study shows time, distance and cost savings when using drones rather than delivery vans. The model reveals efficiency factors to operate DDS. The study shows the economic necessity for delivering low weight goods via DDS. The primary methodological novelty of this study is a model that integrates factors relevant to drones into the VRP.
The core goal of this paper is to identify guidance on how the research community can better transition their research into payment card fraud detection towards a transformation away from the current unacceptable levels of payment card fraud. Payment card fraud is a serious and long-term threat to society (Ryman-Tubb and d’Avila Garcez, 2010) with an economic impact forecast to be $416bn in 2017 (see Appendix A).1 The proceeds of this fraud are known to finance terrorism, arms and drug crime. Until recently the patterns of fraud (fraud vectors) have slowly evolved and the criminals modus operandi (MO) has remained unsophisticated. Disruptive technologies such as smartphones, mobile payments, cloud computing and contactless payments have emerged almost simultaneously with large-scale data breaches. This has led to a growth in new fraud vectors, so that the existing methods for detection are becoming less effective. This in turn makes further research in this domain important. In this context, a timely survey of published methods for payment card fraud detection is presented with the focus on methods that use AI and machine learning. The purpose of the survey is to consistently benchmark payment card fraud detection methods for industry using transactional volumes in 2017. This benchmark will show that only eight methods have a practical performance to be deployed in industry despite the body of research. The key challenges in the application of artificial intelligence and machine learning to fraud detection are discerned. Future directions are discussed and it is suggested that a cognitive computing approach is a promising research direction while encouraging industry data philanthropy.
You can play against "Edi" a chess program using Matlab. It uses a greedy heuristic to find the "best" move.
As a first contribution the mTSP is solved using an exact method and two heuristics, where the number of nodes per route is balanced. The first heuristic uses a nearest node approach and the second assigns the closest vehicle (salesman). A comparison of heuristics with test-instances being in the Euclidean plane showed similar solution quality and runtime. On average, the nearest node solutions are approximately one percent better. The closest vehicle heuristic is especially important when the nodes (customers) are not known in advance, e.g. for online routing. Whilst the nearest node is preferable when one vehicle has to be used multiple times to service all customers. The second contribution is a closed form formula that describes the mTSP distance dependent on the number of vehicles and customers. Increasing the number of salesman results in an approximately linear distance growth for uniformly distributed nodes in a Euclidean grid plane. The distance growth is almost proportional to the square root of number of customers (nodes). These two insights are combined in a single formula. The minimum distance of a node to n uniformly distributed random (real and integer) points was derived and expressed as functional relationship dependent on the number of vehicles. This gives theoretical underpinnings and is in agreement with the distances found via the previous mTSP heuristics. Hence, this allows to compute all expected mTSP distances without the need of running the heuristics.