Title Details

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.

Preface xi

Notes on Contributors xv

Part I THE CONTRIBUTIONS OF INTELLIGENT TECHNIQUES IN MULTICRITERIA DECISION AIDING 1

1 Computational intelligence techniques for multicriteria decision aiding: An overview 3
Michael Doumpos and Constantin Zopounidis

1.1 Introduction 3

1.2 The MCDA paradigm 4

1.2.1 Modeling process 4

1.2.2 Methodological approaches 6

1.3 Computational intelligence in MCDA 9

1.3.1 Statistical learning and data mining 9

1.3.2 Fuzzy modeling 12

1.3.3 Metaheuristics 15

1.4 Conclusions 17

References 18

2 Intelligent decision support systems 25
Gloria Phillips-Wren

2.1 Introduction 25

2.2 Fundamentals of human decision making 26

2.3 Decision support systems 29

2.4 Intelligent decision support systems 30

2.4.1 Artificial neural networks for intelligent decision support 31

2.4.2 Fuzzy logic for intelligent decision support 34

2.4.3 Expert systems for intelligent decision support 35

2.4.4 Evolutionary computing for intelligent decision support 35

2.4.5 Intelligent agents for intelligent decision support 36

2.5 Evaluating intelligent decision support systems 38

2.5.1 Determining evaluation criteria 38

2.5.2 Multi-criteria model for IDSS assessment 39

2.6 Summary and future trends 40

Acknowledgment 41

References 41

Part II INTELLIGENT TECHNOLOGIES FOR DECISION SUPPORT AND PREFERENCE MODELING 45

3 Designing distributed multi-criteria decision support systems for complex and uncertain situations 47
Tina Comes, Niek Wijngaards and Frank Schultmann

3.1 Introduction 47

3.2 Example applications 49

3.3 Key challenges 51

3.4 Making trade-offs: Multi-criteria decision analysis 53

3.4.1 Multi-attribute decision support 53

3.4.2 Making trade-offs under uncertainty 55

3.5 Exploring the future: Scenario-based reasoning 56

3.6 Making robust decisions: Combining MCDA and SBR 57

3.6.1 Decisions under uncertainty: The concept of robustness 57

3.6.2 Combining scenarios and MCDA 58

3.6.3 Collecting, sharing and processing information: A distributed approach 59

3.6.4 Keeping track of future developments: Constructing comparable scenarios 61

3.6.5 Respecting constraints and requirements: Scenario management 64

3.6.6 Assisting evaluation: Assessing large numbers of scenarios 66

3.7 Discussion 69

3.8 Conclusion 71

Acknowledgment 71

References 72

4 Preference representation with ontologies 77
Aida Valls, Antonio Moreno and Joan Borr`as

4.1 Introduction 77

4.2 Ontology-based preference models 80

4.3 Maintaining the user profile up to date 85

4.4 Decision making methods exploiting the preference information stored in ontologies 88

4.4.1 Recommendation based on aggregation 91

4.4.2 Recommendation based on similarities 92

4.4.3 Recommendation based on rules 93

4.5 Discussion and open questions 94

Acknowledgments 95

References 96

Part III DECISION MODELS 101

5 Neural networks in multicriteria decision support 103
Thomas Hanne

5.1 Introduction 103

5.2 Basic concepts of neural networks 104

5.2.1 Neural networks for intelligent decision support 109

5.3 Basics in multicriteria decision aid 111

5.3.1 MCDM problems 111

5.3.2 Solutions of MCDM problems 112

5.4 Neural networks and multicriteria decision support 113

5.4.1 Review of neural network applications to MCDM problems 115

5.4.2 Discussion 121

5.5 Summary and conclusions 122

References 123

6 Rule-based approach to multicriteria ranking 127
Marcin Szel ¸ag, Salvatore Greco and Roman S©©owi´nski

6.1 Introduction 127

6.2 Problem setting 130

6.3 Pairwise comparison table 132

6.4 Rough approximation of outranking and nonoutranking relations 133

6.5 Induction and application of decision rules 136

6.6 Exploitation of preference graphs 139

6.7 Illustrative example 149

6.8 Summary and conclusions 155

Acknowledgment 155

References 155

Appendix 159

7 About the application of evidence theory in multicriteria decision aid 161
Mohamed Ayman Boujelben and Yves De Smet

7.1 Introduction 161

7.2 Evidence theory: Some concepts 163

7.2.1 Knowledge model 163

7.2.2 Combination 164

7.2.3 Decision making 165

7.3 New concepts in evidence theory for MCDA 165

7.3.1 First belief dominance 165

7.3.2 RBBD concept 167

7.4 Multicriteria methods modeled by evidence theory 169

7.4.1 Evidential reasoning approach 169

7.4.2 DS/AHP 172

7.4.3 DISSET 174

7.4.4 A choice model inspired by ELECTRE I 176

7.4.5 A ranking model inspired by Xu et al.’s method 179

7.5 Discussion 181

7.6 Conclusion 183

References 183

Part IV MULTIOBJECTIVE OPTIMIZATION 189

8 Interactive approaches applied to multiobjective evolutionary algorithms 191
Antonio L´opez Jaimes and Carlos A. Coello Coello

8.1 Introduction 191

8.1.1 Methods analyzed in this chapter 192

8.2 Basic concepts and notation 193

8.2.1 Multiobjective optimization problems 193

8.2.2 Classical interactive methods 195

8.3 MOEAs based on reference point methods 196

8.3.1 A weighted distance metric 196

8.3.2 Light beam search combined with NSGA-II 198

8.3.3 Controlling the accuracy of the Pareto front approximation 198

8.3.4 Light beam search combined with PSO 199

8.3.5 A preference relation based on a weighted distance metric 199

8.3.6 The Chebyshev preference relation 200

8.4 MOEAs based on value function methods 202

8.4.1 Progressive approximation of a value function 202

8.4.2 Value function by ordinal regression 202

8.5 Miscellaneous methods 203

8.5.1 Desirability functions 203

8.6 Conclusions and future work 204

Acknowledgment 205

References 205

9 Generalized data envelopment analysis and computational intelligence in multiple criteria decision making 209
Yeboon Yun and Hirotaka Nakayama

9.1 Introduction 209

9.2 Generalized data envelopment analysis 211

9.2.1 Basic DEA models: CCR, BCC and FDH models 212

9.2.2 GDEA model 214

9.3 Generation of Pareto optimal solutions using GDEA and computational intelligence 217

9.3.1 GDEA in fitness evaluation 217

9.3.2 GDEA in deciding the parameters of multi-objective PSO 222

9.3.3 Expected improvement for multi-objective optimization using GDEA 225

9.4 Summary 229

References 231

10 Fuzzy multiobjective optimization 235
Masatoshi Sakawa

10.1 Introduction 235

10.2 Solution concepts for multiobjective programming 236

10.3 Interactive multiobjective linear programming 237

10.4 Fuzzy multiobjective linear programming 241

10.5 Interactive fuzzy multiobjective linear programming 242

10.6 Interactive fuzzy multiobjective linear programming with fuzzy parameters 248

10.7 Interactive fuzzy stochastic multiobjective linear programming 257

10.8 Related works and applications 266

References 267

Part V APPLICATIONS IN MANAGEMENT AND ENGINEERING 273

11 Multiple criteria decision aid and agents: Supporting effective resource federation in virtual organizations 275
Pavlos Delias and Nikolaos Matsatsinis

11.1 Introduction 275

11.2 The intuition of MCDA in multi-agent systems 276

11.3 Resource federation applied 277

11.3.1 Describing the problem in a cloud computing context 277

11.3.2 Problem modeling 278

11.3.3 Assessing agents’ value function for resource federation 279

11.4 An illustrative example 281

11.5 Conclusions 283

References 283

12 Fuzzy analytic hierarchy process using type-2 fuzzy sets: An application to warehouse location selection 285
I¢«rem Uc¸al Sar©¥, Bas¸ar O¨ ztays¸i, and Cengiz Kahraman

12.1 Introduction 285

12.2 Multicriteria selection 287

12.2.1 The ELECTRE method 289

12.2.2 PROMETHEE 289

12.2.3 TOPSIS 289

12.2.4 The weighted sum model method 290

12.2.5 Multi-attribute utility theory 290

12.2.6 Analytic hierarchy process 291

12.3 Literature review of fuzzy AHP 292

12.4 Buckley’s type-1 fuzzy AHP 293

12.5 Type-2 fuzzy sets 296

12.6 Type-2 fuzzy AHP 298

12.7 An application: Warehouse location selection 299

12.8 Conclusion 304

References 304

13 Applying genetic algorithms to optimize energy efficiency in buildings 309
Christina Diakaki and Evangelos Grigoroudis

13.1 Introduction 309

13.2 State-of-the-art review 312

13.3 An example case study 316

13.3.1 Basic principles and problem definition 316

13.3.2 Decision variables 318

13.3.3 Decision criteria 318

13.3.4 Decision model 320

13.4 Development and application of a genetic algorithm for the example case study 323

13.4.1 Development of the genetic algorithm 323

13.4.2 Application of the genetic algorithm, analysis of results and discussion 328

13.5 Conclusions 330

References 331

14 Nature-inspired intelligence for Pareto optimality analysis in portfolio optimization 335
Vassilios Vassiliadis and Georgios Dounias

14.1 Introduction 335

14.2 Literature review 336

14.3 Methodological issues 338

14.4 Pareto optimal sets in portfolio optimization 339

14.4.1 Pareto efficiency 339

14.4.2 Mathematical formulation of the portfolio optimization problem 340

14.5 Computational results 341

14.5.1 Experimental setup 341

14.5.2 Efficient frontier 342

14.6 Conclusion 344

References 345

Index 347

“To conclude this review, I recommend that departmental libraries buy this book for its presentation of state-of-the-art methodologies in multicriteria decision analysis as it relates to AI.”  (Interfaces, 1 July 2015)

"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." (Zentralblatt MATH 2016)