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Multicriteria decision aid and artificial intelligence : links, theory and applications / edited by Michael Doumpos and Evangelos Grigoroundis

Publisher	Chichester, West Sussex, U.K : John Wiley
Year	2013
Authors	Doumpos, Michael Grigoroudis, Evangelos

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Links to the text	Location	Volume	Call No.	Barcode No.	Status	Comments	ISBN	Printed	Restriction	Reserve
Links to the text	Library Off-campus access			OB00188411		Wiley Online Library (電子ブック)	9781118522509

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Material Type	E-Book
Media type	機械可読データファイル
Size	1 online resource
Contents	CONTRIBUTIONS OF INTELLIGENT TECHNIQUES IN MULTICRITERIA DECISION AIDING / Constantin Zopounidis Computational intelligence techniques for multicriteria decision aiding: An overview / Gloria Phillips-Wren Introduction / Frank Schultmann MCDA paradigm / Joan Borras Modeling process / Thomas Hanne Methodological approaches / Roman Stowinski Computational intelligence in MCDA / Yves De Smet Statistical learning and data mining / Carlos A. Coello Coello Fuzzy modeling / Hirotaka Nakayama Metaheuristics / Masatoshi Sakawa Conclusions References Intelligent decision support systems Introduction Fundamentals of human decision making Decision support systems Intelligent decision support systems Artificial neural networks for intelligent decision support Fuzzy logic for intelligent decision support Expert systems for intelligent decision support Evolutionary computing for intelligent decision support Intelligent agents for intelligent decision support Evaluating intelligent decision support systems Determining evaluation criteria Multi-criteria model for IDSS assessment Summary and future trends Acknowledgment References INTELLIGENT TECHNOLOGIES FOR DECISION SUPPORT AND PREFERENCE MODELING Designing distributed multi-criteria decision support systems for complex and uncertain situations Introduction Example applications Key challenges Making trade-offs: Multi-criteria decision analysis Multi-attribute decision support Making trade-offs under uncertainty Exploring the future: Scenario-based reasoning Making robust decisions: Combining MCDA and SBR Decisions under uncertainty: The concept of robustness Combining scenarios and MCDA Collecting, sharing and processing information: A distributed approach Keeping track of future developments: Constructing comparable scenarios Respecting constraints and requirements: Scenario management Assisting evaluation: Assessing large numbers of scenarios Discussion Conclusion Acknowledgment References Preference representation with ontologies Introduction Ontology-based preference models Maintaining the user profile up to date Decision making methods exploiting the preference information stored in ontologies Recommendation based on aggregation Recommendation based on similarities Recommendation based on rules Discussion and open questions Acknowledgments References DECISION MODELS Neural networks in multicriteria decision support Introduction Basic concepts of neural networks Neural networks for intelligent decision support Basics in multicriteria decision aid MCDM problems Solutions of MCDM problems Neural networks and multicriteria decision support Review of neural network applications to MCDM problems Discussion Summary and conclusions References Rule-based approach to multicriteria ranking Introduction Problem setting Pairwise comparison table Rough approximation of outranking and nonoutranking relations Induction and application of decision rules Exploitation of preference graphs Illustrative example Summary and conclusions Acknowledgment References Appendix About the application of evidence theory in multicriteria decision aid Introduction Evidence theory: Some concepts Knowledge model Combination Decision making New concepts in evidence theory for MCDA First belief dominance RBBD concept Multicriteria methods modeled by evidence theory Evidential reasoning approach DS/AHP DISSET choice model inspired by ELECTRE I ranking model inspired by Xu et al.'s method Discussion Conclusion References MULTIOBJECTIVE OPTIMIZATION Interactive approaches applied to multiobjective evolutionary algorithms Introduction Methods analyzed in this chapter Basic concepts and notation Multiobjective optimization problems Classical interactive methods MOEAs based on reference point methods weighted distance metric Light beam search combined with NSGA-II Controlling the accuracy of the Pareto front approximation Light beam search combined with PSO preference relation based on a weighted distance metric Chebyshev preference relation MOEAs based on value function methods Progressive approximation of a value function Value function by ordinal regression Miscellaneous methods Desirability functions Conclusions and future work Acknowledgment References Generalized data envelopment analysis and computational intelligence in multiple criteria decision making Introduction Generalized data envelopment analysis Basic DEA models: CCR, BCC and FDH models GDEA model Generation of Pareto optimal solutions using GDEA and computational intelligence GDEA in fitness evaluation GDEA in deciding the parameters of multi-objective PSO Expected improvement for multi-objective optimization using GDEA Summary References Fuzzy multiobjective optimization Introduction Solution concepts for multiobjective programming Interactive multiobjective linear programming Fuzzy multiobjective linear programming Interactive fuzzy multiobjective linear programming Interactive fuzzy multiobjective linear programming with fuzzy parameters Interactive fuzzy stochastic multiobjective linear programming Related works and applications References V / Nikolaos Matsatsinis APPLICATIONS IN MANAGEMENT AND ENGINEERING / Cengiz Kahraman Multiple criteria decision aid and agents: Supporting effective resource federation in virtual organizations / Evangelos Grigoroudis Introduction / Georgios Dounias intuition of MCDA in multi-agent systems Resource federation applied Describing the problem in a cloud computing context Problem modeling Assessing agents' value function for resource federation illustrative example Conclusions References Fuzzy analytic hierarchy process using type-2 fuzzy sets: An application to warehouse location selection Introduction Multicriteria selection ELECTRE method PROMETHEE TOPSIS weighted sum model method Multi-attribute utility theory Analytic hierarchy process Literature review of fuzzy AHP Buckley's type-1 fuzzy AHP Type-2 fuzzy sets Type-2 fuzzy AHP application: Warehouse location selection Conclusion References Applying genetic algorithms to optimize energy efficiency in buildings Introduction State-of-the-art review example case study Basic principles and problem definition Decision variables Decision criteria Decision model Development and application of a genetic algorithm for the example case study Development of the genetic algorithm Application of the genetic algorithm, analysis of results and discussion Conclusions References Nature-inspired intelligence for Pareto optimality analysis in portfolio optimization Introduction Literature review Methodological issues Pareto optimal sets in portfolio optimization Pareto efficiency Mathematical formulation of the portfolio optimization problem Computational results Experimental setup Efficient frontier Conclusion References
Notes	Online resource; title from digital title page (viewed on Feb. 27, 2013) 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 handlin Includes bibliographical references and index John Wiley and Sons Wiley Online Library: Complete oBooks HTTP:URL=https://onlinelibrary.wiley.com/doi/book/10.1002/9781118522516
Subjects	LCSH:Multiple criteria decision making LCSH:Artificial intelligence FREE:BUSINESS & ECONOMICS -- Statistics All Subject Search FREE:Artificial intelligence FREE:Multiple criteria decision making FREE:Electronic books
Classification	LCC:T57.95 DC23:658.4/033
ID	8000087654
ISBN	9781118522509