Modelling complex ethical decision problems with operations research

This paper discusses the practical contribution of operations research (OR) techniques to modelling decision-making problems with ethical dimensions. Such problems are frequent in the global world: they frequently appear today in sustainability issues, e.g., in conflicts in the triangle of society, economy and environment. We show that the prerequisites for ethical problem-modelling are: the definition of moral principles, the evaluation of the decision context, the participation of stakeholders, the multidisciplinary collection of data, and the understanding of systemic interconnections. Classical OR instruments, mainly used in logistics and optimisation problems, are not entirely satisfactory for coping with the new ethical dimensions of sustainability. It is recommended to use and to develop more advanced, or combined instruments from the multi-criteria/multi-stakeholder and systemic streams of OR. It is argued that an important added value of using OR techniques for modelling today ethical issues lies at least as much in the discovery of open questions as in finding closed-form solutions.     

Ethical guidelines and codes in operations research

We review past endeavors by ORSs to establish ethical guidelines or codes to which their members must subscribe to. We contrast such activities with those of the international community of related professional societies. It is clear that ORSs world-wide do not exhibit the important basic characteristic of a profession, that is, the adoption of and adherence to ethical guidelines and codes. Such adoptions by OR professional organizations are rare.     

A bibliometric analysis of operations research and management science

Bibliometric analysis is the quantitative study of bibliographic material. It provides a general picture of a research field that can be classified by papers, authors and journals. This paper presents a bibliometric overview of research published in operations research and management science in recent decades. The main objective of this study is to identify some of the most relevant research in this field and some of the newest trends according to the information found in the Web of Science database. Several classifications are made, including an analysis of the most influential journals, the two hundred most cited papers of all time and the most productive and influential authors. The results obtained are in accordance with the common wisdom, although some variations are found.     

Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management

Retail requires efficient decision support to manage increasing product proliferation and various consumer choice effects with limited shelf space. Our goal is to identify, describe and compare decision support systems for category planning. This research analyzes quantitative models and software applications in assortment and shelf space management and contributes to a more integrated modeling approach. There are difficulties commonly involved in the use of commercial software and the implementation and transfer of scientific models. Scientific decision models either focus on space-dependent demand or substitution effects, whereas software applications use simplistic rules of thumb. We show that retail assortment planning models neglect space-elastic demand and largely also ignore constraints of limited shelf space. Shelf space management streams on the other hand, mostly omit substitution effects between products when products are delisted orout-of-stock, which is the focus of consumer choice models in assortment planning. Also, the problem sizes of the models are often not relevant for realistic category sizes. Addressing these issues, this paper provides a state-of-the-art overview and research framework for integrated assortment and shelf space planning.     

Group decision making on water resources based on analysis of individual rankings

In problems to do with managing water resources multiple decision makers are involved, each acting in their own right and using different value systems. In the literature on management science, several procedures are proposed in order to establish a collective preference based on the aggregation of different individual preferences. However, the well-known methods that focus on a single winner have some inconveniences that should be addressed. This paper is focused on a group decision making procedure based on the analysis of individual rankings with the aim of choosing an appropriate alternative for a water resources problem. This alternative is found to be the best compromise from the points of view of all actors involved in the decision problem. The structure of the method is set out as is its application to the water resources problem. A comparison with other methods is presented and discussed.     

A comparison of simplified value function approaches for treating uncertainty in multi-criteria decision analysis

Uncertainty is present in many decisions where an action's consequences are unknown because they depend on future events. Multi-attribute utility theory (MAUT) offers an axiomatic basis for choice, but practitioners may prefer to use simpler decision models for transparency, ease of use, or other practical reasons. We identify some ‘simplified’ models currently in use and use a simulation experiment to evaluate their ability to approximate results obtained using MAUT. Our basic message is that avoiding assessment errors in the application of a simplified model is more important than the choice of a particular type of model, but that the best performance over a range of decision problems is from a model using a small number of quantiles.     

Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS

This paper reviews a variety of studies in which multi-attribute utility theory (MAU) has been used successfully to aid group decision processes and discusses the incorporation of MAU in Group Decision Support Systems (GDSS). GDSS of various levels of automation have been proposed to aid group decision making. A number of commercial GDSSs are available which are capable of aiding generation of alternatives, their evaluation, and selection. Many authors have promoted the incorporation of quantitative decision making models in GDSS. The most appropriate quantitative tool for GDSS is MAU, but MAU has rarely been utilized. The cases reviewed illustrate that the use of MAU techniques is not prohibitively difficult or complex.     

A location analysis approach for military maintenance scheduling with geographically dispersed service areas

This research uses a two-stage maximal covering location problem (MCLP) model to develop Inter continental ballistic missile (ICBM) maintenance schedules for the US Air Force. Solutions are compared to actual missile maintenance activities accomplished at F. E. Warren Air Force Base (AFB), Wyoming in May 2005. Sensitivity analysis is performed to determine the impact of altering security response times and the number of security patrol areas on the quality of daily maintenance schedules and personnel usage. Results indicate marked improvement over traditional Air Force scheduling methods. In addition sensitivity analysis identifies the levels at which the quality and quantity of maintenance performance is impacted.     

Policies for knowledge refreshing in databases

Knowledge discovery in databases (KDD) provides organizations necessary tools to sift through vast data stores to extract knowledge. This process supports and improves decision making in organizations. In this paper, we introduce and define the concept of knowledge refreshing, a critical step to ensure the quality and timeliness of knowledge discovered in a KDD process. This has been unfortunately overlooked by prior researchers. Specifically, we study knowledge refreshing from the perspective of when to refresh knowledge so that the total system cost over a time horizon is minimized. We propose a model for knowledge refreshing, and a dynamic programming methodology for developing optimal strategies. We demonstrate the effectiveness of the proposed methodology using data from a real world application. The proposed methodology provides decision makers guidance in running KDD effectively and efficiently.     

BlueLinx can benefit from innovative inventory management methods for commodity forward buys

Commodity prices often fluctuate significantly from one purchasing opportunity to the next. These fluctuations allow firms to benefit from forward buying (buying for future demand in addition to current demand) when prices are low. We propose a combined heuristic to determine the optimal number of future periods a firm should purchase at each ordering opportunity in order to maximize total expected profit when there is uncertainty in future demand and future buying price. We compare our heuristic with existing methods via simulation using real demand data from BlueLinx, a two-stage distributor of building products. The results show that our combined heuristic performs better than any existing methods considering forward buying or safety stock separately. We also compare our heuristic to the optimal inventory management policy by full enumeration for a smaller data set. The proposed heuristic is shown to be close to optimal. This study is the first to decide both the optimal number of future periods to buy for uncertain purchase price and the appropriate purchasing quantity with safety stock for uncertain demand simultaneously. The experience suggests that the proposed combined heuristic is simple and can be very beneficial for any company where forward buying is possible.     

A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating demand are taken into account to develop a stochastic operational optimization of staffing levels. Mathematically, the problem is stated as a mixed-integer linear programming problem. Simulations with store data belonging to a major Swiss retailer show the effective performance of the proposed approach. The schedule quality is assessed through comparison with a deterministic scheduling package, which has been used at several outlets in Switzerland.     

Mapping the changes in management science: A review of ‘soft’ OR/MS articles published in Omega (1973–2008)

The paper proposes a framework to map the development of management science (MS) and uses it to survey the MS literature published in Omega, the International Journal of Management Science. The management science discourse over the last 35 years is mapped in four paradigms: (1) optimisation/normative; (2) interpretative/learning; (3) critical; and (4) a post-modern management science approach. Using a set of keywords and authors’ names associated with the main features of each MS discourse, articles published between 1973 and 2008 are grouped. Results seem to suggest that Omega, a US-based journal, has been mainly developing publications under the optimisation paradigm; only very few papers adhering to the interpretative/learning and critical paradigms have been given space in Omega. The handful of papers from the interpretative paradigm are seen as attempts of UK-based academics to open some space for ‘soft’ operational research (OR) or ‘problem structuring methods’ in a positivistic outlook that seems to pervade US management science practice. Results of the survey and features of MS literature published in Omega are discussed. The paper aims to raise awareness amongst management scientist, systems and operational researchers of the differences between the MS/OR practice in both countries.     

Incorporating preference information in interactive reference point methods for multiobjective optimization

In this paper, we introduce new ways of utilizing preference information specified by the decision maker in interactive reference point based methods. A reference point consists of desirable values for each objective function. The idea is to take the desires of the decision maker into account more closely when projecting the reference point onto the set of nondominated solutions. In this way we can support the decision maker in finding the most satisfactory solutions faster. In practice, we adjust the weights in the achievement scalarizing function that projects the reference point. We identify different cases depending on the amount of additional information available and demonstrate the cases with examples. Finally, we summarize results of extensive computational tests that give evidence of the efficiency of the ideas proposed.     

A grounded theory research approach to building and testing TQM theory in operations management

Total quality management (TQM) has continued to develop as a strategic business improvement approach in organisations and within the Operations Management literature. Strategic TQM is a dynamic phenomenon, reflecting the complexity and technology development in the business environment. Therefore, this conception of TQM has led to significant challenges with regards to developing suitable models and research methodology where traditional, and normative research data, includes survey responses associated with deductive theory and testing. Such data, and its use, is often premised on cause and effect rationality and fails to supply deep rich data to address meanings, phenomena and complex socio-political events, which is a feature of strategic TQM.     

At the boundary: Post-COVID agenda for business and management research in Europe and beyond

A rapidly growing body of scholarly research discusses the impact of the COVID-19 pandemic on business and management research. This Reflection on Europe takes stock of the situation and reflects critically on the deeper changes to the business ecosystem which the pandemic may engender. Based on surface level observations in three different contexts of business research, the analysis uncovers changes to underlying market circumstances which point towards radical shifts in the boundary conditions between business and society. The article concludes with suggestions for a post-COVID research agenda.     

Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

How smart cities transform operations models: a new research agenda for operations management in the digital economy

Abstract

The notion of smart cities is growing in prominence in the digital economy. The integration of urban infrastructures with information and communication technologies enables the development of new operations models. Digitised infrastructures offer opportunities for public and private organisations to design and deliver more customer-centric products or services, particularly for those that require geographical proximity with consumers in the online to offline (O2O) context. A framework is developed and used to analyse three case examples. These cases illustrate the emergence of new operations models and, demonstrate how smart cities are redefining the characteristics of operations models around their scalability, analytical output and connectivity. We also explore the feasibility, vulnerability and acceptability of each new operation. This paper contributes to our understanding of how smart cities can potentially transform operational models, and sets out a research agenda for operations management in smart cities in the digital economy.     

European management teaching and research: Reflections on the life and work of A. Blanqui

Europe’s first historian of political thought, author of the preface to The Wealth of Nations, and also the first figure to acquire a business school in his lifetime and become its dean, Adolphe Blanqui (1798-1854) has nevertheless become a somewhat neglected figure. As we approach the bicentenary of the school he presided – the oldest of the great 19th century management training institutes, which still exists today – in this essay I propose to place his life and work in context before exploring the principal thrust of his educational philosophy and his lasting heritage. Blanqui was a great European, devoted to certain principles in the education of future entrepreneurs and ‘traders’: combatting ugliness, learning how to deal with difference, and leading by example while also keeping pace with technical evolutions. I conclude by insisting on the importance, then and now, of situating management training within the context of geopolitical, aesthetic and ethical factors.