Links, Theory and Applications
Buch, Englisch, 368 Seiten, Format (B × H): 176 mm x 251 mm, Gewicht: 691 g
ISBN: 978-1-119-97639-4
Verlag: Turner Publishing Company
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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(c)(c)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(c)¥, 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
