Formal decision and evaluation models are so widespread that almost no one can pretend not to have used or suffered the consequences of one of them. This book is a guide aimed at helping the analyst to choose a model and use it consistently. A sound analysis of techniques is proposed and the presentation can be extended to most decision and evaluation models as a "decision aiding methodology".

During the past two decades, the consideration of mUltiple objectives in modeling and decision making has grown by leaps and bounds. The nineties in particular have seen the emphasis shift from the dominance of single-objective modeling and optimization toward an emphasis on multiple objectives. The proceedings of this Conference epitomize these evolutionary changes and contribute to the important role that the tield of multiple criteria decision making (MCDM) now plays in planning, design, operational, management, and policy decisions. Of special interest are the contributions of MCDM to manufacturing engineering. For example, it has recently been recognized that optimal, single-objective solutions have often been pursued at the expense of the much broader applicability of designs and solutions that satisfy multiple objectives. In particular, the theme (MCDM and Its Worldwide Role in Risk-Based Decision Making) of the XIVth International Conference on Multiple Criteria Decision Making (Charlottesville, Virginia, USA, June 8-12, 1998) represents the growing importance of risk-cost-benefit analysis in decision making and in engineering design and manufacturing. In such systems, minimizing the of rare and extreme events emerges as an essential objective that risk complements the minimization of the traditional expected value of risk, along with the objectives attached to cost and performance. These proceedings include forty-five papers that were presented at the Conference. A variety of techniques have been proposed for solving multiple criteria decision-making problems. The emphasis and style of the different techniques largely reflect the fields of expertise of their developers.

This book is an outgrowth of formal graduate courses in multiple-criteria decision making (MCDM) that the author has taught at the University of Rochester, University of Texas at Austin, and University of Kansas since 1972. The purpose is, on one hand, to offer the reader an integral and systematic view of various concepts and techniques in MCDM at an "introductory" level, and, on the other hand, to provide a basic conception of the human decision mechanism, which may improve our ability to apply the techniques we have learned and may broaden our llJ.ind for modeling human decision making. The book is written with a goal in mind that the reader should be able to assimilate and benefit from most of the concepts in the book if he has the mathematical maturity equivalent to a course in operations research or optimiz ation theory. Good training in linear and nonlinear programming is sufficient to digest, perhaps easily, most of the concepts in the book.

The objective of this conference was to foster a healthy exchange of ideas and experience in the domain of multiple criteria problem solving. This conference was an outgrowth of an earlier conference I organized with Herve Thiriez at CESA, Jouy-en-Josas, France in 1975 during my stay at the European Institute in Brussels. When I re joined the State University of New York at Buffalo that year, I be gan to search for potential sponsors for this conference. Approxi mately one year later when the prospects began to look promising, I contacted several individuals to act as an informal coordinating committee for the conference. I wanted to avoid biasing the con ference completely to my way of thinking! The members of this committee were Jim Dyer, Peter Fishburn, Ralph Kee. ney, Bernard Roy (Universite de Paris IX Dauphine who was unable to participate in the conference), and Milan Zeleny. Though the committee did not meet, per se, their inputs regarding format, possible participants, number of participants, length of the conference, and so on were of great value to me in planning and organizing the conference. I wish to acknowledge the contributions of this group. We were most fortunate in obtaining the financial support of the European Institute for Advanced Studies in Management, Brussels ·(one of the sponsors of the Jouy-en-Josas conference), the Office of Naval Research, and the State University of New York at Buffalo.

The generalized area of multiple criteria decision making (MCDM) can be defined as the body of methods and procedures by which the concern for multiple conflicting criteria can be formally incorporated into the analytical process. MCDM consists mostly of two branches, multiple criteria optimization and multi-criteria decision analysis (MCDA). While MCDA is typically concerned with multiple criteria problems that have a small number of alternatives often in an environment of uncertainty (location of an airport, type of drug rehabilitation program), multiple criteria optimization is typically directed at problems formulated within a mathematical programming framework, but with a stack of objectives instead of just one (river basin management, engineering component design, product distribution). It is about the most modern treatment of multiple criteria optimization that this book is concerned. I look at this book as a nicely organized and well-rounded presentation of what I view as ”new wave” topics in multiple criteria optimization. Looking back to the origins of MCDM, most people agree that it was not until about the early 1970s that multiple criteria optimization c- gealed as a field. At this time, and for about the following fifteen years, the focus was on theories of multiple objective linear programming that subsume conventional (single criterion) linear programming, algorithms for characterizing the efficient set, theoretical vector-maximum dev- opments, and interactive procedures.

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems.