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Michael Doumpos, Technical University of Crete, Department of Production Engineering and Management, Greece.
Evangelos Grigoroudis, Technical University of Crete, Department of Production Engineering and Management, Greece.
List of Contributors
Preface
Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding
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
1.3 Computational Intelligence in MCDA
1.3.1 Statistical Learning and Data Mining
1.3.2 Fuzzy Modeling
1.3.3 Metaheuristics
1.4 Conclusions
References
2 Intelligent Decision Support Systems
2.1 Introduction
2.2 Fundamentals of Human Decision Making
2.3 Decision Support System
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
References
Part Two Intelligent Technologies for Decision Support and Preference Modeling
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
3.7 Discussion
3.8 Conclusion
References
4 Preference Representation with Ontologies
4.1 Introduction
4.1.1 Structure of the Chapter
4.2 Ontology-based Preference Models
4.3 Maintaining the User’s 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
References
Part Three Decision Models
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
6 Rule-Based Approach to Multicriteria Ranking
6.1 Introduction
6.2 Problem Setting
6.3 Pairwise Comparison Table (PCT)
6.4 Rough Approximation of Outranking and Non-outranking Relations
6.5 Induction and Application of Decision Rules
6.6 Exploitation of Preference Graphs
6.7 Illustrative Example
6.8 Summary and Conclusions
References
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 Four Multiobjective Optimization
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
8.3 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
References
9 Generalized DEA 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 Generalized DEA 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
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 Five Applications in Management and Engineering
11 MCDA & Agents: Supporting Effective Resource Federation in Virtual Organizations
11.1 Introduction
11.2 The Intuition of Multiple Criteria Decision Aid 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
11.4 An Illustrative Example
11.5 Conclusions
References
12 Fuzzy AHP Using Type II Fuzzy Sets: An Application to Warehouse Location Selection
12.1 Introduction
12.2 Multicriteria Selection
12.2.1 The ELECTRE (Élimination Et Choix Traduisant la Réalité) Method
12.2.2 PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations)
12.2.3 TOPSIS (Technique for Order Preference by Similarity to Ideal Situation)
12.2.4 The WSM (Weighted Sum Model) Method
12.2.5 MAUT (Multi-attribute Utility Theory)
12.2.6 AHP (Analytic Hierarchy Process)
12.3 Literature Review on 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
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
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
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