Multicriteria Decision Aid and Artificial Intelligence : Links, Theory and Applications.
Title:
Multicriteria Decision Aid and Artificial Intelligence : Links, Theory and Applications.
Author:
Doumpos, Michael.
ISBN:
9781118522509
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (369 pages)
Contents:
Cover -- Title Page -- Copyright -- Contents -- Preface -- Notes on Contributors -- Part I The Contributions of Intelligent Techniques in Multicriteria Decision Aiding -- Chapter 1 Computational intelligence techniques for multicriteria decision aiding: An overview -- 1.1 Introduction -- 1.2 The MCDA paradigm -- 1.2.1 Modeling process -- 1.2.2 Methodological approaches -- 1.2.2.1 Multiobjective mathematical programming -- 1.2.2.2 Multiattribute utility/value theory -- 1.2.2.3 Outranking techniques -- 1.2.2.4 Preference disaggregation analysis -- 1.3 Computational intelligence in MCDA -- 1.3.1 Statistical learning and data mining -- 1.3.1.1 Artificial neural networks -- 1.3.1.2 Rule-based models -- 1.3.1.3 Kernel methods -- 1.3.2 Fuzzy modeling -- 1.3.2.1 Fuzzy multiobjective optimization -- 1.3.2.2 Fuzzy preference modeling -- 1.3.3 Metaheuristics -- 1.3.3.1 Evolutionary methods and metaheuristics in multiobjective optimization -- 1.3.3.2 Preference disaggregation with evolutionary techniques -- 1.4 Conclusions -- References -- Chapter 2 Intelligent decision support systems -- 2.1 Introduction -- 2.2 Fundamentals of human decision making -- 2.3 Decision support systems -- 2.4 Intelligent decision support systems -- 2.4.1 Artificial neural networks for intelligent decision support -- 2.4.2 Fuzzy logic for intelligent decision support -- 2.4.3 Expert systems for intelligent decision support -- 2.4.4 Evolutionary computing for intelligent decision support -- 2.4.5 Intelligent agents for intelligent decision support -- 2.5 Evaluating intelligent decision support systems -- 2.5.1 Determining evaluation criteria -- 2.5.2 Multi-criteria model for IDSS assessment -- 2.6 Summary and future trends -- Acknowledgment -- References.
Part II Intelligent Technologies for Decision Support and Preference Modeling -- Chapter 3 Designing distributed multi-criteria decision support systems for complex and uncertain situations -- 3.1 Introduction -- 3.2 Example applications -- 3.3 Key challenges -- 3.4 Making trade-offs: Multi-criteria decision analysis -- 3.4.1 Multi-attribute decision support -- 3.4.2 Making trade-offs under uncertainty -- 3.5 Exploring the future: Scenario-based reasoning -- 3.6 Making robust decisions: Combining MCDA and SBR -- 3.6.1 Decisions under uncertainty: The concept of robustness -- 3.6.2 Combining scenarios and MCDA -- 3.6.3 Collecting, sharing and processing information: A distributed approach -- 3.6.4 Keeping track of future developments: Constructing comparable scenarios -- 3.6.5 Respecting constraints and requirements: Scenario management -- 3.6.6 Assisting evaluation: Assessing large numbers of scenarios -- 3.6.6.1 Comparing single scenarios: Exploring the stability of consequences -- 3.6.6.2 Considering multiple scenarios: Aggregation techniques -- 3.7 Discussion -- 3.8 Conclusion -- Acknowledgment -- References -- Chapter 4 Preference representation with ontologies -- 4.1 Introduction -- 4.2 Ontology-based preference models -- 4.3 Maintaining the user profile up to date -- 4.4 Decision making methods exploiting the preference information stored in ontologies -- 4.4.1 Recommendation based on aggregation -- 4.4.2 Recommendation based on similarities -- 4.4.3 Recommendation based on rules -- 4.5 Discussion and open questions -- Acknowledgments -- References -- Part III Decision Models -- Chapter 5 Neural networks in multicriteria decision support -- 5.1 Introduction -- 5.2 Basic concepts of neural networks -- 5.2.1 Neural networks for intelligent decision support -- 5.3 Basics in multicriteria decision aid.
5.3.1 MCDM problems -- 5.3.2 Solutions of MCDM problems -- 5.4 Neural networks and multicriteria decision support -- 5.4.1 Review of neural network applications to MCDM problems -- 5.4.2 Discussion -- 5.5 Summary and conclusions -- References -- Chapter 6 Rule-based approach to multicriteria ranking -- 6.1 Introduction -- 6.2 Problem setting -- 6.3 Pairwise comparison table -- 6.4 Rough approximation of outranking and nonoutranking relations -- 6.5 Induction and application of decision rules -- 6.6 Exploitation of preference graphs -- 6.7 Illustrative example -- 6.8 Summary and conclusions -- Acknowledgment -- References -- Appendix -- Chapter 7 About the application of evidence theory in multicriteria decision aid -- 7.1 Introduction -- 7.2 Evidence theory: Some concepts -- 7.2.1 Knowledge model -- 7.2.2 Combination -- 7.2.3 Decision making -- 7.3 New concepts in evidence theory for MCDA -- 7.3.1 First belief dominance -- 7.3.2 RBBD concept -- 7.4 Multicriteria methods modeled by evidence theory -- 7.4.1 Evidential reasoning approach -- 7.4.2 DS/AHP -- 7.4.3 DISSET -- 7.4.4 A choice model inspired by ELECTRE I -- 7.4.5 A ranking model inspired by Xu et al.'s method -- 7.5 Discussion -- 7.6 Conclusion -- References -- Part IV Multiobjective Optimization -- Chapter 8 Interactive approaches applied to multiobjective evolutionary algorithms -- 8.1 Introduction -- 8.1.1 Methods analyzed in this chapter -- 8.2 Basic concepts and notation -- 8.2.1 Multiobjective optimization problems -- 8.2.2 Classical interactive methods -- 8.2.2.1 Reference point methods -- 8.2.2.2 Light beam search method -- 8.3 MOEAs based on reference point methods -- 8.3.1 A weighted distance metric -- 8.3.2 Light beam search combined with NSGA-II -- 8.3.3 Controlling the accuracy of the Pareto front approximation.
8.3.4 Light beam search combined with PSO -- 8.3.5 A preference relation based on a weighted distance metric -- 8.3.6 The Chebyshev preference relation -- 8.4 MOEAs based on value function methods -- 8.4.1 Progressive approximation of a value function -- 8.4.2 Value function by ordinal regression -- 8.5 Miscellaneous methods -- 8.5.1 Desirability functions -- 8.6 Conclusions and future work -- Acknowledgment -- References -- Chapter 9 Generalized data envelopment analysis and computational intelligence in multiple criteria decision making -- 9.1 Introduction -- 9.2 Generalized data envelopment analysis -- 9.2.1 Basic DEA models: CCR, BCC and FDH models -- 9.2.2 GDEA model -- 9.3 Generation of Pareto optimal solutions using GDEA and computational intelligence -- 9.3.1 GDEA in fitness evaluation -- 9.3.2 GDEA in deciding the parameters of multi-objective PSO -- 9.3.3 Expected improvement for multi-objective optimization using GDEA -- 9.4 Summary -- References -- Chapter 10 Fuzzy multiobjective optimization -- 10.1 Introduction -- 10.2 Solution concepts for multiobjective programming -- 10.3 Interactive multiobjective linear programming -- 10.4 Fuzzy multiobjective linear programming -- 10.5 Interactive fuzzy multiobjective linear programming -- 10.6 Interactive fuzzy multiobjective linear programming with fuzzy parameters -- 10.7 Interactive fuzzy stochastic multiobjective linear programming -- 10.8 Related works and applications -- References -- Part V Applications in Management and Engineering -- Chapter 11 Multiple criteria decision aid and agents: Supporting effective resource federation in virtual organizations -- 11.1 Introduction -- 11.2 The intuition of MCDA in multi-agent systems -- 11.3 Resource federation applied -- 11.3.1 Describing the problem in a cloud computing context -- 11.3.2 Problem modeling.
11.3.3 Assessing agents' value function for resource federation -- 11.3.3.1 Robustness analysis -- 11.4 An illustrative example -- 11.5 Conclusions -- References -- Chapter 12 Fuzzy analytic hierarchy process using type-2 fuzzy sets: An application to warehouse location selection -- 12.1 Introduction -- 12.2 Multicriteria selection -- 12.2.1 The ELECTRE method -- 12.2.2 PROMETHEE -- 12.2.3 TOPSIS -- 12.2.4 The weighted sum model method -- 12.2.5 Multi-attribute utility theory -- 12.2.6 Analytic hierarchy process -- 12.3 Literature review of fuzzy AHP -- 12.4 Buckley's type-1 fuzzy AHP -- 12.5 Type-2 fuzzy sets -- 12.6 Type-2 fuzzy AHP -- 12.7 An application: Warehouse location selection -- 12.8 Conclusion -- References -- Chapter 13 Applying genetic algorithms to optimize energy efficiency in buildings -- 13.1 Introduction -- 13.2 State-of-the-art review -- 13.3 An example case study -- 13.3.1 Basic principles and problem definition -- 13.3.2 Decision variables -- 13.3.3 Decision criteria -- 13.3.4 Decision model -- 13.4 Development and application of a genetic algorithm for the example case study -- 13.4.1 Development of the genetic algorithm -- 13.4.2 Application of the genetic algorithm, analysis of results and discussion -- 13.5 Conclusions -- References -- Chapter 14 Nature-inspired intelligence for Pareto optimality analysis in portfolio optimization -- 14.1 Introduction -- 14.2 Literature review -- 14.3 Methodological issues -- 14.4 Pareto optimal sets in portfolio optimization -- 14.4.1 Pareto efficiency -- 14.4.2 Mathematical formulation of the portfolio optimization problem -- 14.5 Computational results -- 14.5.1 Experimental setup -- 14.5.2 Efficient frontier -- 14.6 Conclusion -- References -- Index.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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