Search

Many policy problems are complex in the sense that natural, technological, social and human elements interact. Problem exploration and structuring are essential as a basis for deliberate and focused approaches towards problem resolution. The results of problem exploration efforts can be laid down in the form of a policy issue paper. We have developed a systemic, stepwise approach, which has been elaborated and taught for over a decade to hundreds of students. This seven-step approach centers on the construction of a system diagram as a means to provide structure to the conceptualisation of a complex problem situation. The approach is based on a conscious combination of existing relatively straightforward analytical methods including objectives hierarchy, means-ends analysis, causal diagramming, stakeholder analysis, and contextual scenarios. The obtained insights are then summed up in a policy issue paper, which is the basis for further planning and research.

Why should anyone spend ?80 on this book? This is not intended as a criticism of either the Dutch or the German Operational Research Societies, of whose 1993 conference these proceedings are a permanent record. No; it is a comment on the academic book publishing industry, which chooses to make a compilation of the papers presented at a conference, sticks a (paperback) cover on them and charges a fancy price to libraries that feel obliged to buy the result. For my money, I should far rather take out a subscription to Interfaces or Operations Research and receive a stream of refereed papers that covers much the same material. Unfortunately, the European Journal of Operational Research has priced itself out of the personal subscription market, so I cannot recommend the natural home of the best of these papers as an alternative to buying this volume. Having put this volume in context, what more is there to say? Well I think that it actually shows Operational Research to be doing rather better on the Continent than it is in the UK. By that I mean that there is more 'real OR' being done, with real clients paying real money (or spending significant amounts of time with academics) to have their problems tackled. True, there is the same hefty bias among contributors to this volume towards academia as in the UK, but one is not left with the impression that OR is a subject to be practised by consenting adults in the privacy of their own universities. Even so, in his plenary lecture 'The Practice of OR', Gerrit Timmer quotes Alexander Rinnooy Kan

Barber Ellis Fine Papers (BEFP) is a Canadian distributor of about 10,000 SKUs to several thousand small or medium-sized firms in the printing and graphic arts industries. A considerable increase in sales is anticipated over the next few years, requiring BEFP to make some important policy decisions in inventory, warehousing and transportation. Two methodologies were employed in our analysis. First, a spreadsheet model was used to simulate several particular inventory/distribution alternatives, e.g. the present decentralized system and a system based on a national distribution centre. Secondly, linear programming was used to obtain the best system alternative, consistent with constraints on capacities, demands, etc. Details of the methods, the results and conclusions are given. Relative merits of simulation and optimization models for this type of inventory/distribution problem are noted, and suggestions are made for further research.

The use of forest fuel is increasing at heating plants in Sweden. Heating plants provide energy in the form of hot water for heating houses and apartments in local municipalities. Forest fuel are products obtained from harvesting in forests that cannot be used for further processing at sawmills and pulp and paper mills. Examples of such products are tree branches, tree tops and low quality logs. The optimization of the supply chain for round-wood (logs to sawmills, pulp and paper mills) and for forest fuel is similar but involves two main differences. First, forest fuel has to be converted into chips before delivery to the customer, and second, the demand for forest fuel varies over the year due to the temperature. To balance the chipping and transportation capacities over time, it is important to manage inventory levels at terminals. The optimization model developed provides decision support for questions regarding the choice of technology for chipping, where to perform the chipping operations, and the allocation of different assortments to heating plants. The system has been tested on a large case study from a Swedish forest energy company. The results show large savings and that the system is very useful for both planning and business development.

A large number of studies have applied simulation to a multitude of issues relating to healthcare. These studies have been published in a number of unrelated publishing outlets, which may hamper the widespread reference and use of such resources. In this paper, we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high-quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies’ results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied to healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques in solving diverse healthcare problems.

Serious information security breaches have caused firms to suffer from customer churns directly or indirectly. To prevent customer churns, firms usually enhance their security protection through two measures, i.e. security investment and security information sharing. Prior studies seldom consider security environment and business environment simultaneously when making a firm’s optimal security decisions. Using game theory, this paper purports to demonstrate that a firm’s security decisions under a competitive environment differ significantly from those under an integrated environment. Moreover, distortions may surface if firms do not cooperate on security practices. Thus, this paper further analyses the measures that a social planner such as the government or industry association controls firms’ security decisions, and results show that these measures may not always be effective. Instead, social planners are recommended to enhance or attenuate the controlling level of the two security decisions ba...

A simulation study consists of several stages: problem formulation, model implementation, verification and validation, experimentation and output data analysis. The application of multiple techniques in the model implementation stage is referred to as hybrid simulation, which we distinguish in this paper from a hybrid M&S study, the latter referring to studies that apply methods and techniques from disciplines like Operations Research (OR), Systems Engineering and Computer Science to one or more stages of a simulation study. Our paper focuses on the contribution of soft OR methods in the problem formulation stage of a simulation study (and by extension a hybrid M&S study). Soft Systems Methodology (SSM) has, arguably, been the most widely used qualitative approach for eliciting system requirements. In this paper, we present Qualitative System Dynamics (QSD), a soft systems method, as having potential use in the problem formulation stage of a healthcare M&S study. The contribution of this paper is thus twofold: (1) a review of the literature in SSM for healthcare operations management and (2) an examination of QSD as an additional soft OR method, complementing (rather than supplanting) existing approaches, which can further aid the understanding of the system in the problem formulation/conceptual modelling stage of a hybrid M&S study.

Ageing populations, rapid technological progress and recent public budget cuts currently threaten the sustainability of public health systems. To meet growing needs with declining resources, decision-makers must identify new ways to avoid reducing the quality of services offered to citizens. This paper focuses on the so-called “co-payment” tools aimed to obtain additional resources for the public health budget directly from citizens. Whereas certain forms of co-payments have always been introduced within health systems to prevent moral hazard behaviours, other co-payment mechanisms are explicitly intended to help finance public healthcare systems. Literature and empirical findings do not agree about the final impact of such co-payment tools, particularly whether they can attain system sustainability and guarantee equitably delivered services. In this paper, we develop an agent-based simulation model which can be used by decision-makers as a decision support tool to compare different co-payment rules and evaluate their impact on the public budget and the health expense of different groups of citizens.

In this paper, we solve the Vehicle Routing Problem with Lunch Break (VRPLB), which arises when drivers must take pauses during their shift, for example, for lunch breaks. Driver breaks have already been considered in long haul transportation when drivers must rest during their travel, but the underlying optimization problem remains difficult and few contributions can be found for less than truckload and last mile distribution contexts. This problem, which appears in the furniture delivery industry, includes rich features such as time windows and heterogeneous vehicles. In this paper, we evaluate the performance of a new mathematical formulation for the VRPLB and of a fast and high performing heuristic. The mixed integer linear programming formulation has the disadvantage of roughly doubling the number of nodes, and thus significantly increasing the size of the distance matrix and the number of variables. Consequently, standard branch-and-bound algorithms are only capable of solving small-sized instances. In order to tackle large instances provided by an industrial partner, we propose a fast multi-start randomized local search heuristic tailored for the VRPLB, which is shown to be very efficient. Through a series of computational experiments, we show that solving the VRPLB without explicitly considering the pauses during the optimization process can lead to a number of infeasibilities. These results demonstrate the importance of integrating drivers pauses in the resolution process.

A service supply chain, which is a system formed by a network of suppliers, service designers, service providers and other service partners, aims to transfer available scarce resources into services and deliver them to satisfy customer needs. It is a known fact that service supply chain management is playing a more and more important role in modern economies. However, the unique features of service supply chains also create new challenges that call for proper management of the respective operations. In particular, how to coordinate a service supply chain with risk considerations is a critical issue. In this paper, we concisely examine coordination and risk management challenges in service supply chain systems. We also introduce the technical papers featured in the special issue.

This paper provides an account of an ongoing project with an independent school in the UK. The project focuses on a strategy development intervention which, from the start, was systemic in orientation. The intention was to integrate simple systems concepts and approaches into the strategy development process to: address power relations in actively engaging a wide range of stakeholders with the school’s strategy-making process; generate a range of good ideas; and make the strategy-making process transparent in order to inspire stakeholder confidence in, and commitment to, it and its outcomes. This paper describes how seeking to meet these aims entailed a series of workshops during the course of which an awareness of the relevance, in our interpretation, of Complex Adaptive Systems concepts grew.

In an emerging knowledge-based economy, knowledge is a critical source of competitive advantage of organizations. This paper addresses two different strategies based on the tacitness of knowledge, indicating how the dilemmas of the choice of knowledge strategies might be resolved. A successful symbiosis strategy based upon a conducive organizational culture can ease knowledge replication within the organization but presents difficulty in imitation by competitors. Such a culture needs to be pervasive within the organization, but at the same time idiosyncratic and unique to the organization. Optimal knowledge management strategies can cultivate the organizational cultural fit, and maximize the organizational profit. The paper provides researchers and practitioners valuable insights to understand the fundamental relationship between knowledge management and organizational culture, and practical guidance to adopt an appropriate knowledge management strategy to exploit organizational knowledge.

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 13/11/2013, available online: https://www.tandfonline.com/doi/full/10.1057/jors.2013.145 This paper provides a systems-based approach to the exploration of the relationship and integration between Supplier Relationship Management (SRM) factors as part of a Sustainable Operations Management (SOM) agenda. The authors have chosen electronic procurement (e-Procurement) as a suitable context in this light. Through a review of extant literature, a Systems Archetype (SA) model was developed (based on the ‘Accidental Adversaries’ archetype) and findings from a quantitative pilot study exploring key factors pertinent to e-Procurement SRM were gathered, and hence evaluated against SOM factors. The objective of this research was to describe and visualise the causal interrelationships involved in SRM-SOM through the application of a SA (as an Operations Research tool). The authors believe that this research also provides a unique approach to developing and harnessing the useful and unique properties of Systems Thinking (ST), by attempting to reduce and organise the (generally ad hoc and wide-ranging) sequence of subjective perspectives commonly experienced in causal mapping experiments. The paper builds upon the extant literature, and provides further basis for continuing research in the areas of ST, SAs and the application of operational research to plan sustainable operations.

Many operational research (OR) techniques use historical data to populate model input parameters. Although the majority of these models take into account stochastic variation of the inputs, they do not necessarily take into account seasonal variations and other stochastic effects that might arise. One of the major applications of OR lies within healthcare, where ever increasing pressure on healthcare systems is having major implications on those who plan the provision of such services. Coping with growing demand for healthcare, as well as the volatile nature of the number of arrivals at a healthcare facility makes modelling healthcare provision one of the most challenging fields of OR. This paper proposes the use of a relatively modern time series technique, Singular Spectrum Analysis (SSA), to improve existing algorithms that give required staffing levels. The methodology is demonstrated using data from a large teaching hospital's emergency unit. Using time dependent queueing theory, as well as SSA, staffing levels are obtained. The performance of our technique is analysed using a weighted mean square error measure, introduced in this paper.

Patient no-show in outpatient clinics has been a long recognized issue, which negatively impacts clinic operational efficiency in terms of costs and patient access to care. One way to reduce these negative impacts is to allow urgent walk-ins during a clinic day. Some clinics allow random walk-ins and some purposely leave open time slots to accommodate them. The objective of this paper is to develop a cost-effective urgent care policy that is added on top of a full schedule and takes into account scheduled patients’ no-show rates to improve patient access to care in a dynamic clinic environment. The findings indicate that the proposed approach outperforms the current random and urgent slot approaches. This paper demonstrates a dynamic approach for accommodating urgent patients into a patient scheduling system, based on the prediction of an individual patient's no-show probability and the maximum number of urgent patients allowed.

This paper presents a real-world examination timetabling problem from Universiti Malaysia Pahang (UMP), Malaysia. The problem involves assigning invigilators to examination rooms. This problem has received less attention than the examination timetabling problem from the research community partly because no data sets are available in the literature. In modelling, and solving, this problem we assume that there is already an examination timetable in place (this was the subject of our previous work) and the task is to assign invigilators to that timetable. The contributions of this paper are to formally define the invigilator scheduling problem and to present a constructive algorithm that is able to produce good quality solutions that are superior to the solutions produced when using the university's current software. We also include additional constraints taking into account the comments made by the invigilators, which the current system fails to capture. The model we present, we believe, accurately reflects the real-world problem, capturing various aspects of the problem that have not been presented before in the scientific literature. Moreover, the proposed approach adheres to all hard constraints, which the university's current system fails to do.

There are many successful evolutionary computation techniques for automatic program generation, with the best known, perhaps, being genetic programming. Genetic programming has obtained human competitive results, even infringing on patented inventions. The majority of the scientific literature on automatic program generation employs such population-based search approaches, to allow a computer system to search a space of programs. In this paper, we present an alternative approach based on local search. There are many local search methodologies that allow successful search of a solution space, based on maintaining a single incumbent solution and searching its neighbourhood. However, use of these methodologies in searching a space of programs has not yet been systematically investigated. The contribution of this paper is to show that a local search of programs can be more successful at automatic program generation than current nature inspired evolutionary computation methodologies.

Despite its huge potential in risk analysis, the Dempster–Shafer Theory of Evidence (DST) has not received enough attention in construction management. This paper presents a DST-based approach for structuring personal experience and professional judgment when assessing construction project risk. DST was innovatively used to tackle the problem of lacking sufficient information through enabling analysts to provide incomplete assessments. Risk cost is used as a common scale for measuring risk impact on the various project objectives, and the Evidential Reasoning algorithm is suggested as a novel alternative for aggregating individual assessments. A spreadsheet-based decision support system (DSS) was devised to facilitate the proposed approach. Four case studies were conducted to examine the approach's viability. Senior managers in four British construction companies tried the DSS and gave very promising feedback. The paper concludes that the proposed methodology may contribute to bridging the gap between theory and practice of construction risk assessment.

Rough set theory has been combined with intuitionistic fuzzy sets in dealing with uncertainty decision making. This paper proposes a general decision-making framework based on the intuitionistic fuzzy rough set model over two universes. We first present the intuitionistic fuzzy rough set model over two universes with a constructive approach and discuss the basic properties of this model. We then give a new approach of decision making in uncertainty environment by using the intuitionistic fuzzy rough sets over two universes. Further, the principal steps of the decision method established in this paper are presented in detail. Finally, an example of handling medical diagnosis problem illustrates this approach.

Since the decline of the Cold War, the risk of major conflict between powerful, industrialized nations has decreased. Insecurities in the 21st century are more apt to arise from the debris of imploding states. Such situations may require intervention—military or otherwise—by concerned states. To meet this new operational challenge, nations must adapt their planning procedures to account for Security, Stabilization, Transition, and Reconstruction Operations (SSTRO). This paper develops a project scheduling framework for post-conflict reconstruction that schedules reconstruction activities to maximize the positive impact to the local economy during the initial phase of SSTRO. Specifically, this paper builds on the Multimode Resource Constrained Project Scheduling Problem with Generalized Precedence Relations (MM-RCPSP-GPR) using goal programming to maximize the reconstruction operations’ positive impact to local population's economic welfare. This MM-RCPSP-GPR variant is applied to a notional example to illustrate its potential use in post-conflict SSTRO. The basic framework can be extended to other settings.

This paper addresses a key issue in the health OR literature, namely the apparent failure of OR modelling to become embedded and widely implemented within healthcare organisations. The research presented here is a case study to evaluate the adoption of one particular simulation modelling tool, Scenario Generator (S:G), which was developed by the SIMUL8 Corporation in a PPI partnership with the UK's National Health Service (NHS) Institute for Innovation and Improvement. The study involved semi-structured interviews with employees of 28 Primary Care Trusts who had all been engaged in some way with the initiative, with participants classified as ‘Not Started’, ‘Given Up’ and ‘Actively Using’. This paper presents a brief summary of barriers and facilitators to the successful use of the S:G software, but its main purpose is to focus more broadly on factors influencing the successful adoption of simulation tools in general within healthcare organisations. The insights gained in this study are relevant to improving the uptake of OR modelling in general within the NHS.

In contemporary military endeavours, Command and Control (C2) arrangements generally aim to ensure an appropriate regulation of command-decision autonomy such that decision makers are able to act in a way that is consistent with the overall set of commanders’ intents and according to the nature of the unfolding situation. This can be a challenge, especially in situations with increasing degrees of uncertainty, ambiguity and complexity, also where individual commanders are faced with conflicting objectives. Increasingly, it seems that command decisions are being taken under conditions of internal command contention; for example, when the likely successful outcome of a tactical mission can often be at odds with the overall strategic and political aims of the campaign. The work in the paper builds on our previous research in decision making under uncertainty and conflicting objectives, where we analysed the responses of military commanders in decision experiments. We demonstrated how multi-attribute utility theory could be used to represent and understand the effects of uncertainty and conflicting objectives on a particular commander's choices. In this paper, we further develop and generalize the theory to show that the geometrical forms of expected utilities, which arise from the assumption of commander rationality, are qualitatively stable in a wide range of scenarios. This opens out into further analysis linking to Catastrophe Theory as it relates to C2 regulatory frameworks for devolving command decision freedoms. We demonstrate how an appreciation of this geometry can aid understanding of the relationship between socially complex operational environments and the prevailing C2, which can also inform selection and training of personnel, to address issues of devolving command decision-rights, as appropriate for the endeavour as a whole. The theory presented in the paper, therefore, provides a means to explore and gain insight into different approaches to regulation of C2 decision making aimed ultimately at achieving C2 agility, or at least at a conceptual language to allow its formal representation. C2 regulatory agents are discussed in terms of detailed functions for moderating command decision making, as appropriate for the degrees of uncertainty and goal contention being faced. The work also begins to address implications of any lack of experience and any differences in personality-type of the individual commanders with respect to risk-taking, open-mindedness and creativity.

In the widely studied ‘revenue sharing’ (hereafter [RS]) contract format, the manufacturer of a product not only charges the retailer a unit wholesale price w, but also requires the retailer to share part of the product's revenue (ie, the unit retail price p) with him. For a product with price-dependent demand, it is well known that if a dominant manufacturer knows the system parameters deterministically, then [RS] gives him the perfect power of simultaneously coordinating the channel and allocating profit arbitrarily. Unfortunately, [RS]'s power deteriorates as the manufacturer's knowledge of the system parameters becomes increasingly uncertain. This paper shows that this deterioration can be substantially reduced by using slightly modified versions of [RS]; these modifications roughly amount to sharing a retailer's gross profit instead of revenue. In other words, this paper presents simple modifications to the classical [RS], leading to contract formats that perform substantially better under system-parameter uncertainty.

We would like to express our thanks to all those listed below who have refereed papers in 2011. As papers are received from many different countries and on a wide variety of Operational Research to...

Canadian railway companies operate in a capital-intensive segment of the transportation industry. In most railway companies, the covered hopper railcar fleet is one of the larger fleets due to its use in moving grain and potash, commodities that move large volumes of product. This railcar fleet is also difficult to manage due to demand seasonality and joint commodity use. This paper demonstrates how an aggregate planning model can be used to support decision making related to optimization of covered hopper railcar storage. Exploratory research prior to model development involved interviews with company personnel. The model was developed through quantitative research and implemented using spreadsheet optimization. The results indicate that using this model can reduce the total cost of storage through effective planning. The model also provided insight to improve railcar storage such as the elimination of excess storage locations and the need to do further investigation. The company is in the process of implementing suggestions from this paper.

The most essential and alluring characteristic of a security estate is the estate's ability to provide 24-h security to its residents, of which the continual patrolling of roads and paths is vital. The objective of this paper is to address the lack of sufficient patrol route design procedures by presenting a tabu search algorithm capable of generating multiple patrol routes for an estate's security guards. The paper shows that the problem of designing these routes can be modelled as an Arc Routing Problem, specifically as min–max k postmen problems. The algorithm is illustrated with a real problem instance from an estate in Gauteng, South Africa. The patrol routes generated by the algorithm provide a significant improvement in the even patrolling of the road network, and a more balanced work distribution among guards. The algorithm is also tested on several benchmark problems from literature.

This paper reports on the use of an evolutionary algorithm (EA) to search a space of heuristic combinations for the uncapacitated examination timetabling problem. The representation used by an EA has an effect on the difficulty of the search and hence the overall success of the system. The paper examines three different representations of heuristic combinations for this problem and compares their performance on a set of benchmark problems for the uncapacitated examination timetabling problem. The study has revealed that certain representations do result in a better performance and generalization of the hyper-heuristic. An EA-based hyper-heuristic combining the use of all three representations (CEA) was implemented and found to generalize better than the EA using each of the representations separately.

This paper examines the long-standing operational issue of patients boarding in the emergency department (ED), who have been admitted to hospital (inpatient ‘boarders’). From this analysis we design a conceptual model that provides a roadmap to create sustainable improvements in ED waiting times. The conceptual model is built using system dynamics methodology, and illustrates the use of system archetypes, a set of common causal feedback loops that illustrate how well-intended decisions have unintentional side effects. This paper outlines the journey taken by one large academic health centre to address these issues, and highlights the larger implications and recommendations that are applicable to other publicly funded hospitals.

This paper addresses the problem of partitioning a local service region into nonoverlapping work areas in which pickups and deliveries are made throughout the day. For a fleet of homogeneous vehicles, a given set of customers, and expected demand for service, the objective is to find the least number of work areas or clusters that satisfy a variety of geometric and capacity constraints. Using rectangles as the basic shape, each cluster must have an aspect ratio that falls within certain bounds, as well as meet load and time requirements dictated by the capacity of a vehicle and the working hours in a day, respectively. The latter requirement presents a unique hurdle because travel times are a function of the actual routes followed by the drivers, and are not known, even in a probabilistic sense, until the clusters are formed. A novel aspect of the paper is the method proposed for dealing with this uncertainty. The problem is modelled using a compact set-covering formulation and is solved with an adaptive column generation heuristic. Because it is not possible to efficiently represent all the constraints in algebraic form, thus allowing a Dantzig-Wolfe decomposition, a constructive approach was taken. The first step involved generating a subset of attractive clusters from seed customers scattered throughout the service region and then iteratively pricing them out to obtain a relaxed solution to the set-covering model. To find integer solutions, a three-phase variable fixing scheme was designed with the aim of balancing solution quality with runtimes. The full methodology was tested on six data sets provided by an internationally known express package carrier. The results showed that vehicle reductions averaging 7.6% could be realized by adopting the configurations derived from the proposed approach.

Clinical engineering departments in hospitals are responsible for establishing and regulating a Medical Equipment Management Program to ensure that medical devices are safe and reliable. In order to mitigate functional failures, significant and critical devices should be identified and prioritized. In this paper, we present a multi-criteria decision-making model to prioritize medical devices according to their criticality. Devices with lower criticality scores can be assigned a lower priority in a maintenance management program. However, those with higher scores should be investigated in detail to find the reasons for their higher criticality, and appropriate actions, such as ‘preventive maintenance’, ‘user training’, ‘redesigning the device’, etc, should be taken. In this paper,we also describe how individual score values obtained for each criterion can be used to establish guidelines for appropriate maintenance strategies for different classes of devices. The information of 26 different medical devices is extracted from a hospital's maintenance management system to illustrate an application of the proposed model.

This paper investigates and discusses the use of systemic methodologies (SMs) developed in management science/operational research (MS/OR), in particular, those SM that have been informing the complexity inherent in environmental management and sustainable (EM/S) practices. By surveying a sample of the top MS/OR and systems journals, we assess the extent to which systemic management science methodologies developed recently have been used in tackling EM/S problems. Titles and abstracts of EM/S applications published in MS/OR and systems journals between 1989 and 2009 were queried for the occurrence of typical keywords associated with a set of SMs (eg, complexity theory, systems dynamics, soft systems, critical systems, viable systems model). The survey identifies a set of articles representing the practice of either a particular methodology or of a mixture of various SMs in EM/S setting. By assembling and critically reviewing a sample of applications in EM/S the paper hopes to raise awareness among environmentalists, operational researchers and management scientists of the benefits of using systemic approaches developed in MS/OR and, in this way, to encourage further exchange and conversation between these fields of management.

Consumer credit scoring is one of the most successful applications of quantitative analysis in business with nearly every major lender using charge-off models to make decisions. Yet banks do not extend credit to control charge-off, but to secure profit. So, while charge-off models work well in rank-ordering the loan default costs associated with lending and are ubiquitous throughout the industry, the equivalent models on the revenue side are not being used despite the need. This paper outlines a profit-based scoring system for credit cards to be used for acquisition decisions by addressing three issues. First, the paper explains why credit card profit models—as opposed to cost or charge-off models—have been difficult to build and implement. Second, a methodology for modelling revenue on credit cards at application is proposed. Finally, acquisition strategies are explored that use both a spend model and a charge-off model to balance tradeoffs between charge-off, revenue, and volume.

This paper describes the mathematical formulations that have underpinned the development of a strategic-level, personnel sustainability planning tool for the Australian Army. The tool considers personnel sustainability in terms of the dynamics of progression through career profiles and the requirements of the force to meet operational capability demands. It assists in analysing the ability of a force structure (current or future) to meet forecast operational deployments, providing insights into: likely problem areas with regard to sustainability, the effect of changing personnel policies, resource/cost implications and force expansion issues. Utilised in shorter-term planning, the tool allows for rapid force options testing and, in longer-term planning, for detailed sustainability analysis of proposed future force structures. The paper focuses on the basic construction of the model and its mathematical underpinnings, while giving insights into its utility and to how it has been applied.

This paper explores the trends in American and British management science/operational research (MS/OR) during the last 25 years. We argue that British MS/OR has developed a soft and systemic approach to MS/OR practice, which has resulted in the emergence of a number of interpretive and critical-oriented methodologies. American MS/OR practice has remained closed to the positivistic discourse. Using a set of keywords and authors’ names associated with the main features of the interpretive and critical MS discourses, we surveyed articles published in three major US MS/OR journals. We compare these results with trends in the UK MS/OR scene. Findings appear to confirm the different directions taken by the MS/OR practice across the Atlantic. The paper posits possible reasons underpinning these differences: firstly, the particular methodological path followed by the British MS/OR, from early ‘soft systems’ applications in the early 1970s to the now well-established ‘Problem Structuring Methods’; and secondly, continuous engagement between the systems and MS/OR British communities (a dialogue that seems not to have occurred in the US). The paper contributes to a reflection on the MS/OR historical developments and contrasts these developments in both countries, two areas of OR significantly under-researched.

The use of Data Envelopment Analysis (DEA) in the electricity distribution sector has been prolific in the number of papers published in research journals. However, while numerous studies have been documented, they have mostly been summative. Their aim has been predominantly descriptive and classificatory. This paper argues that evaluations of a formative nature are more effective than summative studies in promoting a better understanding of the structures and processes of electricity distribution utilities and, consequently, are more appropriate to contribute to performance improvement. To illustrate the use of DEA for formative evaluation, and highlight some of the difficulties of using DEA in practice, this paper compares the cost-efficiency of the Portuguese electricity distribution companies from 2002 to 2006. A dynamic analysis using Malmquist Indices is also conducted in order to evaluate the changes in productivity over this period. Our analysis shows that the application of DEA for formative purposes meets some difficulties. In particular it shows that while the modelling of productivity/efficiency scores using DEA is relatively straightforward, it is comparatively more difficult to develop models that are economically valid and that produce results with face validity. On the basis of the insights derived from this analysis, the paper provides some recommendations regarding the successful application of DEA for performance improvement.

Hurricane forecasts are intended to convey information that is useful in helping individuals and organizations make decisions. For example, decisions include whether a mandatory evacuation should be issued, where emergency evacuation shelters should be located, and what are the appropriate quantities of emergency supplies that should be stockpiled at various locations. This paper incorporates one of the National Hurricane Center's official prediction models into a Bayesian decision framework to address complex decisions made in response to an observed tropical cyclone. The Bayesian decision process accounts for the trade-off between improving forecast accuracy and deteriorating cost efficiency (with respect to implementing a decision) as the storm evolves, which is characteristic of the above-mentioned decisions. The specific application addressed in this paper is a single-supplier, multi-retailer supply chain system in which demand at each retailer location is a random variable that is affected by the trajectory of an observed hurricane. The solution methodology is illustrated through numerical examples, and the benefit of the proposed approach compared to a traditional approach is discussed.

This paper presents the SR-GCWS-CS probabilistic algorithm that combines Monte Carlo simulation with splitting techniques and the Clarke and Wright savings heuristic to find competitive quasi-optimal solutions to the Capacitated Vehicle Routing Problem (CVRP) in reasonable response times. The algorithm, which does not require complex fine-tuning processes, can be used as an alternative to other metaheuristics—such as Simulated Annealing, Tabu Search, Genetic Algorithms, Ant Colony Optimization or GRASP, which might be more difficult to implement and which might require non-trivial fine-tuning processes—when solving CVRP instances. As discussed in the paper, the probabilistic approach presented here aims to provide a relatively simple and yet flexible algorithm which benefits from: (a) the use of the geometric distribution to guide the random search process, and (b) efficient cache and splitting techniques that contribute to significantly reduce computational times. The algorithm is validated through a set of CVRP standard benchmarks and competitive results are obtained in all tested cases. Future work regarding the use of parallel programming to efficiently solve large-scale CVRP instances is discussed. Finally, it is important to notice that some of the principles of the approach presented here might serve as a base to develop similar algorithms for other routing and scheduling combinatorial problems.

Ant colony optimization (ACO) is a metaheuristic for solving combinatorial optimization problems that is based on the foraging behavior of biological ant colonies. Starting with the 1996 seminal paper by Dorigo, Maniezzo and Colorni, ACO techniques have been used to solve the traveling salesperson problem (TSP). In this paper, we focus on a particular type of the ACO algorithm, namely, the rank-based ACO algorithm for the TSP. In particular, this paper identifies an optimal set of key parameters by statistical analysis applied to results of the rank-based ACO for the TSP. Specifically, for six frequently used TSPs available on the World Wide Web, we will solve a total of 27 000 instances for each problem.

This paper reports the results of an online survey conducted with practitioner members of the UK Operational Research (OR) Society. The purpose of the survey was to explore the current practice of supporting the strategy process in terms of activities supported and tools used. The results of the survey are compared to those of previous surveys to explore developments in, inter alia, the use of management/strategy tools and ‘soft’ Operational Research/Management Science (OR/MS) tools. The survey results demonstrate that OR practitioners actively support the strategy process within their organisations. While a wide variety of tools, drawn from the OR/MS and management/strategy fields, are used to support the strategy process within organisations, the findings suggest that soft OR/MS tools are not regularly used. The findings also demonstrate that tools are combined to support the strategy process from both within and across the OR/MS and management/strategy fields. The paper ends by identifying a number of areas for further research.

Present-day expectations of wide stakeholder involvement in strategy-making processes have been addressed in distinctive ways by the Operational Research (OR) and Organisational Development (OD) communities. This paper describes an intervention supporting a strategy for change in a university where a high degree of participation was essential to address the substantive issues faced following the disappointing outcome of an employee satisfaction survey. The importance of staff satisfaction for achieving good business outcomes is explained in the paper before an account is given of the work that was undertaken using a combination of Large Group Intervention and Problem Structuring Method approaches. The achievement of the project in shifting the locus of perceived control and opening up a more participative strategic debate is next assessed. The case demonstrated the benefit of using an intervention process whose participatory nature mirrored the desired changes in the client organisation, and in which the dramatisation of senior management group commitment underlined determination to effect change. Other implications are discussed: the value of using a novel assemblage of approaches and techniques rather than inflexibly following a single methodology; the potential payoff from closer work between the disciplines of OR and OD; and the observation that there is a real opportunity for the wider use of participative approaches in strategy making, something that OR practitioners are well-placed to provide.

Hedge funds have made a significant impact on the performance of world financial markets in recent times. Our objective in this paper is to develop a robust framework for the evaluation of hedge funds by incorporating a maximum number of performance measures through public data sources. We analyse the hedge fund strategies (styles) using a variety of classical risk-return measures with the help of slack-based Data Envelopment Analysis (DEA) models to determine a unique performance indicator. The main thrust is to investigate the risk return profile of 4730 hedge funds classified under 18 different strategies using multiple inputs and outputs. The originality of the work lies in applying Slack-Based DEA to decipher the risk-return profile of these strategies using advanced risk-return measures such as Value at Risk, drawdown, lower and higher partial moments and skewness. We find that the correlation between the ranking of hedge fund strategies based on Sharpe ratio and the DEA models is very low; at the same time, there is a significant correlation between rankings obtained by the application of DEA using different sets of input/output measures. We have also compared the DEA rankings with other traditional financial ratios such as modified Sharpe ratio, Sortino ratio and Calmar ratio. The paper also studies the impact of events such as the Asian financial crisis on the performance of hedge funds. The study around the event shows that only a relatively small number of strategies performed better during times of turmoil.

This paper describes an experimental method for determining the value of different types of information to military decision makers. The experimental method used a simple scenario and a set of serials constructed from cards, each presenting a single piece of information and presented sequentially. Each of a number of pairs of players were taken through the scenario and asked to judge how they would respond to the situation. This paper extends on the method presented in previous papers to consider the case of a decision in which the response can both increase and decrease as more information is presented. This allows a more general military problem to be considered, that of risk-taking behaviour in response to the possibility of chemical, biological, radiological or nuclear events.

There are two key issues in assuring the accuracy of estimates of performance obtained from a simulation model. The first is the removal of any initialisation bias, the second is ensuring that enough output data is produced to obtain an accurate estimate of performance. This paper is concerned with the first issue, and more specifically warm-up estimation. Our aim is to produce an automated procedure, for inclusion into commercial simulation software, for estimating the length of warm-up and hence removing initialisation bias from simulation output data. This paper describes the extensive literature search that was carried out in order to find and assess the various existing warm-up methods, the process of short-listing and testing of candidate methods. In particular it details the extensive testing of the warm-up MSER-5 method.

The paper reviews three modes of rational inference: deductive, inductive and probabilistic. Many examples of each can be found in scientific endeavour, professional practice and public discourse. However, while the strengths and weaknesses of deductive and inductive inference are well established, the implications of the emerging probabilistic orientation are still being worked through. The paper discusses some of the recent findings in psychology and philosophy, and speculates about the implications for scientific and professional practice in general and OR in particular. It is suggested that the probabilistic orientation and Bayesian approach can provide an epistemological lens through which to view the claims of different approaches to inference. Some suggestions for further research are made.

In this paper, we consider a linear program in which the right hand sides of the constraints are uncertain and inaccurate. This uncertainty is represented by intervals, that is to say that each right hand side can take any value in its interval regardless of other constraints. The problem is then to determine a robust solution, which is satisfactory for all possible coefficient values. Classical criteria, such as the worst case and the maximum regret, are applied to define different robust versions of the initial linear program. More recently, Bertsimas and Sim have proposed a new model that generalizes the worst case criterion. The subject of this paper is to establish the relationships between linear programs with uncertain right hand sides and linear programs with uncertain objective function coefficients using the classical duality theory. We show that the transfer of the uncertainty from the right hand sides to the objective function coefficients is possible by establishing new duality relations. When the right hand sides are approximated by intervals, we also propose an extension of the Bertsimas and Sim's model and we show that the maximum regret criterion is equivalent to the worst case criterion.