Showing papers in "Operations Research and Management Science in 2016"

Robust Discrete Optimization Under Discrete and Interval Uncertainty: A Survey

Abstract: In this chapter a review of recent results on robust discrete optimization is presented. The most popular discrete and interval uncertainty representations are discussed. Various robust concepts are presented, namely the traditional minmax (regret) approach with some of its recent extensions, and several two-stage concepts. A special attention is paid to the computational properties of the robust problems considered.

Green vehicle routing

Abstract: Green vehicle routing is a branch of green logistics which refers to vehicle routing problems where externalities of using vehicles, such as carbon dioxide-equivalents emissions, are explicitly taken into account so that they are reduced through better planning. This chapter presents an overview of some of the recent developments in the area, including the description of some vehicle emission models and their applications in road freight transportation.

Interactive Nonlinear Multiobjective Optimization Methods

Abstract: An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the her or his most satisfactory Pareto optimal solution can be found. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the role of the decision maker is very important in interactive methods, methods presented are classified according to the type of preference information that the decision maker is assumed to provide.

Multiattribute Utility Theory (MAUT)

Abstract: In this chapter, we provide a review of multiattribute utility theory. We begin with a brief review of single-attribute preference theory, and explore preference representations that measure a decision maker’s strength of preference and her preferences for risky alternatives. We emphasize the distinction between these two cases, and then explore the implications for multiattribute preference models. We describe the multiattribute decision problem, and discuss the conditions that allow a multiattribute preference function to be decomposed into additive and multiplicative forms under conditions of certainty and risk. The relationships among these distinct types of multiattribute preference functions are then explored, and issues related to their assessment and applications are surveyed.

Continuous Multiobjective Programming

Abstract: We present our view of the state of the art in continuous multiobjective programming. After an introduction we formulate the multiobjective program (MOP) and define the most important solution concepts in Sect. 18.2. In Sect. 18.3 we summarize properties of efficient and nondominated sets. Optimality conditions are reviewed in Sect. 18.4. The main part of the chapter consists of Sects. 18.5 and 18.6 that deal with solution techniques for MOPs and approximation of efficient and nondominated sets. In Sect. 18.7 we discuss specially-structured problems including linear, nonlinear, parametric, and bilevel MOPs. In Sect. 18.8 we present our perspective on future research directions.

DEA Performance Assessment of Mutual Funds

Abstract: The objectives of this paper are manyfold. First we present a comprehensive review of the literature of DEA models for the performance assessment of mutual funds. Then we discuss the problem of the presence of negative returns in DEA modeling for mutual funds and we identify a DEA model that is financially justified and tackles the issue of negative returns in a natural way. Moreover, we present an empirical application on real market data, considering different risk measures. We consider also different holding periods, which include both a period of financial crisis and one of financial recovery. Moreover, we compare the results of the DEA performance measure with those obtained with traditional financial indicators.

Green Rail Transportation: Improving Rail Freight to Support Green Corridors

Abstract: The objective of this chapter is to illustrate the current state of European rail freight research and how this can improve rail freight to support green and sustainable transport, as promoted by the governments of the European Union (EU). Since much of EU policy is based on EU funded research, a review of such rail- and freight-related research, carried out over the past 20 years under the EU/EC framework programmes, will help to contextualize the challenges faced in promoting the governance of green corridors. International rail freight systems, improvement of rail-road modal transfer points, unexploited markets, vehicle design, train productivity, energy use, signalling and governance of multimodal corridors, are all discussed. We argue that green corridors, as a strategic freight transport policy, can only realistically be implemented alongside improvements to the long distance transportation sector, where rail becomes the backbone network of land surface transportation. Additionally, with the trend towards urbanization, where road would serve pretty much the last mile freight operation, a reliable, efficient, safe and environmentally sound inter-urban long distance rail freight system is needed, to support economic growth and to maintain the quality of life of every EU citizen.

Producing Innovations: Determinants of Innovativity and Efficiency

Abstract: In this chapter, we investigate the knowledge production function, using the Community Innovation Survey, an unbalanced firm-level panel data set collected in the Netherlands between 1994 and 2004. This database allows us to span the entire innovation process from initial resources committed (R&D labor and the accumulated knowledge stock) to the final resulting sales volume of new products. We find that inefficiency accounts for between 50 and 92% of the unexplained between firm and over time variation in innovation output, with changes in efficiency explaining on average 62% of the between-firm variation in innovativeness. We do not find a significant difference in average inefficiency between those that do and those that do not cooperate with competitors. However, although government funding does not affect the marginal productivity of the knowledge stock and research labor, firms receiving government support are more efficient than those that do not. Finally, we find that more competitive firms are more innovative in terms of generating new product sales from innovations.

Metaheuristic Approaches for Scheduling Jobs on Parallel Batch Processing Machines

Abstract: We consider a scheduling problem for parallel identical batch processing machines. A batch is a set of jobs that can be processed at the same time on a single machine. The jobs belong to incompatible job families. Only jobs of the same family can be batched together. We are interested in minimizing the total weighted tardiness (TWT) of the jobs. Problems of this type arise, for instance, in semiconductor manufacturing. Other known occurrence of batch processing machines can be found in gear manufacturing. We describe a genetic algorithm (GA), an ant colony optimization (ACO) approach, and a large neighborhood search (LNS) approach for this scheduling problem. The performance of the three metaheuristic approaches is compared based on randomly generated problem instances. The LNS scheme outperforms the two other metaheuristics and is comparable with a variable neighborhood search (VNS) approach, the best performing heuristic for this scheduling problem from the literature.

From Statistical Decision Theory to Robust Optimization: A Maximin Perspective on Robust Decision-Making

Abstract: As attested by the prevalence of worst-case-based robustness analysis in many fields, Wald’s maximin paradigm (circa 1940) plays a central role in the broad area of decision-making under uncertainty. The objective of this chapter is therefore twofold. First, to examine the basic conceptual and modeling aspects of this ostensibly intuitive, yet controversial paradigm, so as to clarify some of the issues involved in its deployment in decision-making in the face of a non-probabilistic uncertainty. Second, to elucidate the differences between this paradigm and other maximin paradigms, such as those used in error analysis and game theory. We thereby chart the journey of this paradigm from the field of statistical decision theory to that of modern robust optimization, highlighting its use in the latter, as a tool for dealing with both local and global robustness. We also look briefly at the relationship between probabilistic and worst-case-based robustness analysis.

Intergenerational Justice in Protective and Resilience Investments with Uncertain Future Preferences and Resources

Abstract: How much should each generation invest in building resilient infrastructure to protect against possible future natural disasters? If such disasters are infrequent, members of each generation may be tempted to defer investments in resilience and protective infrastructure (e.g., in building or improving dams and levees; retrofitting office and residential buildings; creating more robust transportation, power, and communications networks; etc.) in favor of consumption or growth. Succumbing to this temptation imposes risks on future generations of needlessly large losses or disproportionate need to invest in resilience. Yet, even the most dutiful and altruistic present generation has limited obligations to invest to protect future ones, especially if present investments in resilience reduce growth and future prosperity, or if the preferences, priorities, resources, and capabilities of future generations are highly uncertain. This paper discusses several different frameworks for clarifying how much each generation should invest in protection. Optimal economic growth models provide a well-developed technical framework for maximizing average or minimal expected social utility over time, but require consistency and cooperation over time that may not be psychologically or politically realistic. If investment decisions are viewed as a form of dynamic “dictator game” in which earlier generations choose how to allocate benefits between themselves and later generations, then insights from behavioral economics, risk psychology, and moral psychology suggest cues related to deservingness and trustworthiness that powerfully affect what is perceived as fair and right in such settings. A Rawlsian concept of justice (what investment decision rules would people choose from behind a veil of ignorance, in which no one knew what generation he or she would be born into?) solves the problems of over-discounting long-delayed and uncertain consequences that have frustrated some previous efforts to apply cost-benefit analysis to ethically charged issues involving intergenerational justice. We suggest several principles for applying insights from these different frameworks to investments in building resilient communities and mitigating natural disaster risks across generations.

Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs)

Abstract: The Toyota Prius was first introduced in 1997 and since then over 150 hybrid electric vehicles (HEVs) have been brought to the automobile market around the world. This was spurred by a major interest in the future of vehicles using ‘alternative fuel’ for addressing environmental and fuel dependency concerns. This study evaluates and compares the technological advancement observed in different HEV market segments over the past 15 years. The results indicate that the introduction of a wide range of midsize HEVs is posing a threat to the two-seaters and compact HEV segments while an SUV segment shows a fast adoption with a significant performance improvement. The rates of change for each segment are also provided to give insights into the estimation of the future performance levels for new product development target setting purposes.