The book discusses a new approach to the classification problem following the decision support orientation of multicriteria decision aid. The book reviews the existing research on the development of classification methods, investigating the corresponding model development procedures, and providing a thorough analysis of their performance both in experimental situations and real-world problems from the field of finance. Audience: Researchers and professionals working in management science, decision analysis, operations research, financial/banking analysis, economics, statistics, computer science, as well as graduate students in management science and operations research.
This book provides a new point of view on the subject of business failure prediction, through the application of multicriteria analysis methods. The aim of the book is to provide a review of the research in the area and to explore the adequacy of these methods to one of the most complex problems in the area of financial management. In addition, the book explores the applications of the methods so that it can become a very useful tool for researchers and practitioners. The analysis of the modeling and the results in these applications provides the background for further employment of the methods.
Presents recent advances in both models and systems for intelligent decision making. Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems. The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering. Multicriteria Decision Aid and Artificial Intelligence: Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems. Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.
This volume highlights recent applications of multiple-criteria decision-making (MCDM) models in the field of finance. Covering a wide range of MCDM approaches, including multiobjective optimization, goal programming, value-based models, outranking techniques, and fuzzy models, it provides researchers and practitioners with a set of MCDM methodologies and empirical results in areas such as portfolio management, investment appraisal, banking, and corporate finance, among others. The book addresses issues related to problem structuring and modeling, solution techniques, comparative analyses, as well as combinations of MCDM models with other analytical methodologies.
This book presents the main principles of preference disaggregation analysis and covers theoretical advances in preference modelling, group decision making, classification methods, robustness analysis, process mining, and decision support systems. In addition, it highlights several applications of the preference disaggregation analysis in a wide range of areas, such as customer satisfaction analysis, consumer behavior, energy and environmental policy, strategy development, and agricultural marketing. This book was published in honor of Yannis Siskos on the occasion of his retirement from the University of Piraeus, Greece. It offers a unique snapshot of the preference disaggregation philosophy in multiple criteria decision analysis and presents a range of research ideas, many of which were significantly influenced by Professor Siskos work.
This book provides a new point of view on the subject of business failure prediction, through the application of multicriteria analysis methods. The aim of the book is to provide a review of the research in the area and to explore the adequacy of these methods to one of the most complex problems in the area of financial management. In addition, the book explores the applications of the methods so that it can become a very useful tool for researchers and practitioners. The analysis of the modeling and the results in these applications provides the background for further employment of the methods.
Multi Criteria Decision Making (MCDM) is a generic term for all methods that help people making decisions according to their preferences, in situations where there is more than one conflicting criterion. It is a branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives and criteria. The approach of MCDM involves decision making concerning quantitative and qualitative factors. The importance and success of MCDM are due to the fact that they have successfully dealt with different types of problematics for supporting decision makers such as choice, ranking and sorting, description. Even though, each of the different problematics in MCDM is important, Multi-Criteria Decision-Making Sorting Methods will focus on sorting approaches across a wide range of interesting techniques and research disciplines. The applications which have been and can be solved by these techniques are more and more important in current real-world decision-making problems. Therefore, the book provides a clear overview of MCDM sorting methods and the different tools which can be used to solve real-world problems by revising such tools and characterizing them according to their performance and suitability for different types of problems. The book is aimed at a broad audience including computer scientists, engineers, geography and GIS experts, business and financial management experts, environment experts, and all those professional people interested in MCDM and its applications. The book may also be useful for teaching MCDM courses in fields such as industrial management, computer science, and applied mathematics, as new developments in multi-criteria decision making. Provides insights into the latest research trends in MCDM sorting methods and fuzzy-based approaches Focuses on the application of MCDM sorting methods to GIS based problems Presents engineers, computer scientists and researchers with effective and efficient solutions to real-world problems
This book presents a rich collection of studies on the analysis of sustainable development from a multiple criteria decision-making (MCDM) perspective, written by some of the most prominent authors in the field of MCDM/A. The book constitutes a unique international reference guide to the analysis, measurement, and management of sustainability in a multidimensional decision analysis context. Chiefly intended for academics and policymakers, it reflects some of the latest methodological advances in decision-making, which are illustrated in real-life applications to sustainability-related topics in both the private and public sector.
Multiple Criteria Decision Analysis: State of the Art Surveys provides survey articles and references of the seminal or state-of-the-art research on MCDA. The material covered ranges from the foundations of MCDA, over various MCDA methodologies (outranking methods, multiattribute utility and value theories, non-classical approaches) to multiobjective mathematical programming, MCDA applications, and software. This vast amount of material is organized in 8 parts, with a total of 25 chapters. More than 2000 references are listed.
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.
