Handbook of Multicriteria Analysis

After reviewing what the adjective “robust” means in decision aiding ¹(DA) and explaining why it is important to be concerned about robustness in DA, I present a twofold observation (section 1.2), which leads me to make four proposals in order to better respond to robustness concern in decision aiding. With the first two proposals (sections 1.3 and 1.4), I show that, in many cases, the vague approximations and the zones of ignorance against which robustness helps to prevent, must be considered in terms of substituting the concept of version for the usual concept of scenario and focusing on the diverse processing procedures that must be used to study the decision aiding problem as it was formulated. Next, I show (section1.5) that the traditional responses formulated in terms of “robust solutions” limit the meaning of this concept. I briefly describe a certain number of avenues for research that could be explored further, not only in order to otherwise conceive the solutions that could be qualified as robust in another way, but also to better interact with decision-makers to make them aware that the adjective “robust” can be subjective. Finally, the fourth proposal is related to forms of responses that lead to stating “robust conclusions”, which do not necessarily refer to solutions characterize das robust. After defining what I mean by robust conclusions and giving some examples, I mention the rare approaches that have been proposed for obtaining such conclusions.

In this chapter we discuss the use of MCDA for supporting strategic decision making, particularly within strategy workshops. The chapter begins by exploring the nature of strategic decisions and the characteristics of the strategic decision making process. Specifically, we examine the technical issues associated with the content of strategic decisions, and the social aspects that characterise the processes within which they are created. These features lead us to propose a number of adaptations to the standard MCDA approach if it were to be used at a more strategic level. We make suggestions on how to implement these proposals, and illustrate them with examples drawn from real-world interventions in which we have participate das strategic decision support analysts.

We present main characteristics of ELECTRE family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation in the set of actions – it is constructed in result of concordance and non-discordance tests involving a specific input preference information. After a brief description of the constructivist conception in which the ELECTRE methods are inserted, we present the main features of these methods. We discuss such characteristic features as: the possibility of taking into account positive and negative reasons in the modeling of preferences, without any need for recoding the data; using of thresholds for taking into account the imperfect knowledge of data; the absence of systematic compensation between “gains” and “losses”. The main weaknesses are also presented. Then, some aspects related to new developments are outlined. These are related to some new methodological developments, new procedures, axiomatic analysis, software tools, and several other aspects. The chapter ends with conclusions.

Multicriteria thinking demonstrates that in order to make a best choice in a decision, discussion and cause-effect reasoning are inadequate to learn what the best overall outcome is. The Analytic Hierarchy Process (AHP) and its generalization to dependence and feedback, the Analytic Network Process (ANP), provide a comprehensive structure and mathematics to incorporate measurements for tangible criteria and derive priorities for intangible criteria to enable one to choose a best alternative for a decision. It overcomes so-called bounded rationality that is based on the assumption of transitivity by including in its structures and calculations, the sensitivity and depth of feelings associated with understanding and the imagination and awareness needed to address all the concerns. The AHP can cope with the inherent subjectivity in all decision making, and make it explicit to the stakeholders through relative quantitative priorities. It also provides the means to validate outcomes when measurements are available to show that it does not do number crunching without meaningful justification. It can deal with the benefits, opportunities, costs and risks separately and bring them together to determine the best overall outcome. One can also perform dynamic sensitivity analysis of changes in judgments to ensure that the best outcome is stable. In an award from the Institute for Operations Research and the Management Sciences (INFORMS) given to the author in October 2008 it is written: “The AHP has revolutionized how we resolve complex decision problems... the AHP has been applied worldwide to help decision makers in every conceivable decision context across both the public and private sectors, with literally thousands of reported applications.”

Useful decision recommendations can often be provided even if the model parameters are not exactly specified. The recognition of this fact has spurred the development of multicriteria methods which are capable of admitting and synthesizing incomplete preference information in hierarchical weighting models. These methods share similarities in that they (i) accommodate incomplete preference information through set inclusion, (ii) offer decision recommendations based on dominance concepts and decision rules, and (iii) support the iterative exploration of the decision maker’s preferences. In this Chapter, we review these methods which are jointly referred to by the term ‘preference programming’. Specifically, we discuss the potential benefits of using them, and provide tentative guidelines for their deployment.

The aggregation-disaggregation approaches as an important field of multicriteria decision-aid systems aim to infer global preference models from preference structures, as directly expressed by one or more decision-makers. The main objective of this chapter is to present new research developments of aggregationdisaggregation models and discuss related research topics. These recent developments cover a wide variety of topics, like post-optimality analysis, robustness analysis, group and collective decision-making. They focus mainly on the UTA family of models and highlight their most important advantages: they are flexible in the modeling process of a decision problem, they may provide analytical results that are able to analyze the behavior of the decision-maker, and they can offer alternative ways to reduce the preferential inconsistencies between the decision-maker and the results of the disaggregation model. Finally, future research topics in the context of preference disaggregation approaches are outlined in this chapter.

Disaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this chapter the relationships between the two fields are explored. The differences and similarities between the two approaches are identified and a review is given regarding the integration of the two fields.

We provide an introduction to interactive methods in multiple objective programming¹. Our discussion focuses on the principles to implement such methods. Our purpose is not to review existing procedures, but to provide some examples to illustrate the underlying main ideas. Furthermore, we discuss two available software systems developed to implement interactive methods.

In this chapter Multi-Objective Evolutionary Algorithms (MOEAs) are introduced and some details discussed. A presentation of some of the concepts in which this type of algorithms are based on is given. Then, a summary of the main algorithms behind these approaches and their applications is provided, together with a brief discussion including their advantages and disadvantages, degree of applicability, and some known applications. Finally, future trends in this area and some possible paths for future research are pointed out.

The purpose of the paper is to provide a critical overview of the decisionmaking approach known as Goal Programming (GP). The paper starts by tracing the origins of GP back to work by Charnes and Cooper at the end of the 1950s in fields like non-parametric regression, and the analysis of contradictions in non-solvable linear programming problems. After chronicling its evolution from its original form into a powerful decision-making method, the GP approach is linked with the Simonian bounded rationality theories based upon the ‘satisficing’ concept. In this way, several GP models are presented as fruitful vehicles for implementing this kind of ‘satisficing’ philosophy. The last part of the paper presents some critical issues and extensions of the GP approach. The chapter ends by discussing potential extensions, as well as GP’s role for solving complex real-world problems in the near future.

Living in a vibrant and constantly changing world, in the last few decades we have witnessed tremendous advances in many important areas of human activity including medicine and drugs, public policy and service, engineering and economics, and new computing technologies. While rapid economic growth and substantial technological progress have resulted in a host of new opportunities for many private and public enterprises and organizations, in consequence, today we are also facing enormous competition in international trading, growing degrees of environmental pollution caused by continuing industrialization and urbanization, and unprecedented water and energy demands accelerating the steady shortage of natural resources and food. At the same time, the effects of globalization and the instantaneous exchange of data through modern telecommunication and information systems have radically changed the way we perceive and are able to respond to these and other increasingly complex challenges varying from homeland security and public health over climate, energy and transportation to natural resources and the environment. To solve these challenges, it is beyond doubt that the simultaneous consideration of a large number of interrelated aspects and criteria has become essential to make deliberate decisions and take responsible actions in both our professional as well as our private lives.

This paper presents the application of an approach designed to evaluate non-financial performance in companies. Within a defined perimeter, the approach called EPISSURE produces an ‘evaluation of non-financial performance with a hierarchical set of synthesis indicators co-constructed during a process of framed dialogue.’ The paper discusses how the EPISSURE approach was tested and set up within several companies for the purpose of evaluating sponsorship projects and deciding on their follow-up. Test results seem to indicate that the EPISSURE approach is decidedly appropriate for evaluating non-financial performance.

Despite a vast literature on the capital structure of the firm there still is a big gap between theory and practice. Starting with the seminal work by Modigliani and Miller, much attention has been paid to the optimality of capital structure from the shareholders’ point of view. Over the last few decades studies have been produced on the effect of other stakeholders’ interests on capital structure. Another area that has received considerable attention is the relation between managerial incentives and capital structure. Furthermore, the issue of corporate control and, related, the issue of corporate governance, receive a lion’s part of the more recent academic attention for capital structure decisions. From all these studies, one thing is clear: The capital structure decision (or rather, the management of the capital structure over time) has to deal with more issues than the maximization of the firm’s market value alone. In this paper, we give an overview of the different objectives and considerations that have been proposed in the literature. We show that capital structure decisions can be framed as multiple criteria decision problems which can then benefit from multiple criteria decision support tools that are widely available.

This chapter emphasizes on the major components under which MCDA applications in marketing and e-commerce have been developed and describes characteristic examples of research works that apply MCDA methodologies in marketing and e-commerce. The chapter is divided into two main sections separating the MCDA applications in the marketing discipline from those that appear in the ecommerce field. In each section fundamental notions of marketing and e-commerce are discussed accordingly and some characteristic examples of research works are analytically mentioned. The aim of this work is to endow candidate researchers that are interested in applyingMCDA methodologies in marketing and e-commerce with adequate background information to further develop their scopes and ideas.