Multiple-criteria decision analysis

About: Multiple-criteria decision analysis is a research topic. Over the lifetime, 10706 publications have been published within this topic receiving 302647 citations. The topic is also known as: multiple-criteria decision-making & MCDA.

The Analytic Hierarchy Process

Abstract: This chapter provides an overview of Analytic Hierarchy Process (AHP), which is a systematic procedure for representing the elements of any problem hierarchically. It organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pair-wise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy. These judgments are then translated to numbers. The AHP includes procedures and principles used to synthesize the many judgments to derive priorities among criteria and subsequently for alternative solutions. It is useful to note that the numbers thus obtained are ratio scale estimates and correspond to so-called hard numbers. Problem solving is a process of setting priorities in steps. One step decides on the most important elements of a problem, another on how best to repair, replace, test, and evaluate the elements, and another on how to implement the solution and measure performance.

Multiple criteria decision analysis: state of the art surveys

Abstract: In two volumes, this new edition presents the state of the art in Multiple Criteria Decision Analysis (MCDA). Reflecting the explosive growth in the field seen during the last several years, the editors not only present surveys of the foundations of MCDA, but look as well at many new areas and new applications. Individual chapter authors are among the most prestigious names in MCDA research, and combined their chapters bring the field completely up to date. Part I of the book considers the history and current state of MCDA, with surveys that cover the early history of MCDA and an overview that discusses the “pre-theoretical” assumptions of MCDA. Part II then presents the foundations of MCDA, with individual chapters that provide a very exhaustive review of preference modeling, along with a chapter devoted to the axiomatic basis of the different models that multiple criteria preferences. Part III looks at outranking methods, with three chapters that consider the ELECTRE methods, PROMETHEE methods, and a look at the rich literature of other outranking methods. Part IV, on Multiattribute Utility and Value Theories (MAUT), presents chapters on the fundamentals of this approach, the very well known UTA methods, the Analytic Hierarchy Process (AHP) and its more recent extension, the Analytic Network Process (ANP), as well as a chapter on MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). Part V looks at Non-Classical MCDA Approaches, with chapters on risk and uncertainty in MCDA, the decision rule approach to MCDA, the fuzzy integral approach, the verbal decision methods, and a tentative assessment of the role of fuzzy sets in decision analysis. Part VI, on Multiobjective Optimization, contains chapters on recent developments of vector and set optimization, the state of the art in continuous multiobjective programming, multiobjective combinatorial optimization, fuzzy multicriteria optimization, a review of the field of goal programming, interactive methods for solving multiobjective optimization problems, and relationships between MCDA and evolutionary multiobjective optimization (EMO). Part VII, on Applications, selects some of the most significant areas, including contributions of MCDA in finance, energy planning problems, telecommunication network planning and design, sustainable development, and portfolio analysis. Finally, Part VIII, on MCDM software, presents well known MCDA software packages.

Multiple Criteria Decision Analysis: An Integrated Approach

Abstract: The field of multiple criteria decision analysis (MCDA) - also sometimes termed multiple criteria decision aid, or multiple criteria decision making (MCDM) - has developed rapidly over the past quarter century and in the process a number of divergent schools of thought have emerged. Multiple Criteria Decision Analysis: An Integrated Approach provides a comprehensive yet widely accessible overview of the main streams of thought within MCDA. Two principal aims are: To provide sufficient awareness of the underlying philosophies and theories, understanding of the practical detail of the methods, and insight into practice to enable researchers, students and industry practitioners to implement MCDA methods in an informed manner; To develop an integrated view of MCDA, incorporating both integration of different schools of thought within MCDA and integration of MCDA with broader management theory, science and practice, thereby informing the development of theory and practice across these areas. It is felt that this two-fold emphasis gives a book which will be of value to the following three groups: Practicing decision analysts or graduate students in MCDA for whom this book should serve as a state-of-the-art review, especially as regards techniques outside of their own specialization; Operational researchers or graduate students in OR/MS who wish to extend their knowledge into the tools of MCDA; Managers or management students who need to understand what MCDA can offer them. Review: The book...is an excellent overview of the different multiple criteria approaches developed in divergent schools of thought which emerged in the last three decades. This book is not only a theoretical document, but also provides good coverage of practical issues. It can be recommended to a broad audience, ranging from those in academic institutions to practitioners, as well as those who are interested in finding information on multiple criteria approaches, methods and techniques. It is an excellent book in the main area of Operations Research and Decision Analysis, with a special focus on Multiple Criteria and is suitable for undergraduate and graduate students.' JosA(c) R. Figueira, DIMACS Center, Rutgers University.

Best-worst multi-criteria decision-making method

Abstract: In this paper, a new method, called best-worst method (BWM) is proposed to solve multi-criteria decision-making (MCDM) problems. In an MCDM problem, a number of alternatives are evaluated with respect to a number of criteria in order to select the best alternative(s). According to BWM, the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criteria are identified first by the decision-maker. Pairwise comparisons are then conducted between each of these two criteria (best and worst) and the other criteria. A maximin problem is then formulated and solved to determine the weights of different criteria. The weights of the alternatives with respect to different criteria are obtained using the same process. The final scores of the alternatives are derived by aggregating the weights from different sets of criteria and alternatives, based on which the best alternative is selected. A consistency ratio is proposed for the BWM to check the reliability of the comparisons. To illustrate the proposed method and evaluate its performance, we used some numerical examples and a real-word decision-making problem (mobile phone selection). For the purpose of comparison, we chose AHP (analytic hierarchy process), which is also a pairwise comparison-based method. Statistical results show that BWM performs significantly better than AHP with respect to the consistency ratio, and the other evaluation criteria: minimum violation, total deviation, and conformity. The salient features of the proposed method, compared to the existing MCDM methods, are: (1) it requires less comparison data; (2) it leads to more consistent comparisons, which means that it produces more reliable results.