Multicriteria Decision Aid and Artificial Intelligence

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.Content: Chapter 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview (pages 1–23): Michael Doumpos and Constantin ZopounidisChapter 2 Intelligent Decision Support Systems (pages 25–44): Gloria Phillips?WrenChapter 3 Designing Distributed Multi?Criteria Decision Support Systems for Complex and Uncertain Situations (pages 45–76): Tina Comes, Niek Wijngaards and Frank SchultmannChapter 4 Preference Representation with Ontologies (pages 77–99): Aida Valls, Antonio Moreno and Joan BorrasChapter 5 Neural Networks in Multicriteria Decision Support (pages 101–126): Thomas HanneChapter 6 Rule?Based Approach to Multicriteria Ranking (pages 127–160): Marcin Szela?g, Salvatore Greco and Roman SlowinskiChapter 7 About the Application of Evidence Theory in Multicriteria Decision Aid (pages 161–187): Mohamed Ayman Boujelben and Yves De SmetChapter 8 Interactive Approaches Applied to Multiobjective Evolutionary Algorithms (pages 189–207): Antonio Lopez Jaimes and Carlos A. Coello CoelloChapter 9 Generalized Data Envelopment Analysis and Computational Intelligence in Multiple Criteria Decision Making (pages 209–233): Yeboon Yun and Hirotaka NakayamaChapter 10 Fuzzy Multiobjective Optimization (pages 235–271): Masatoshi SakawaChapter 11 Multiple Criteria Decision Aid and Agents: Supporting Effective Resource Federation in Virtual Organizations (pages 273–284): Pavlos Delias and Nikolaos MatsatsinisChapter 12 Fuzzy Analytic Hierarchy Process Using Type?2 Fuzzy Sets: An Application to Warehouse Location Selection (pages 285–308): Irem Ucal Sary, Basar Oztaysi and Cengiz KahramanChapter 13 Applying Genetic Algorithms to Optimize Energy Efficiency in Buildings (pages 309–333): Christina Diakaki and Evangelos GrigoroudisChapter 14 Nature?Inspired Intelligence for Pareto Optimality Analysis in Portfolio Optimization (pages 335–345): Vassilios Vassiliadis and Georgios Dounias