Multicriteria Decision Aid and Artificial Intelligence

353 Pages·2013·2.926 MB·English

Multicriteria Decision Aid
and Artificial Intelligence
Multicriteria Decision Aid
and Artificial Intelligence
Links, Theory and Applications
Edited by
Michael Doumpos and Evangelos Grigoroudis
Technical University of Crete, Greece
A John Wiley & Sons, Ltd., Publication
Thiseditionfirstpublished2013
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LibraryofCongressCataloging-in-PublicationData
Doumpos,Michael.
Multicriteriadecisionaidandartificialintelligence:links,theoryandapplications/MichaelDoumpos,
EvangelosGrigoroudis.
p.cm.
Includesbibliographicalreferencesandindex.
ISBN978-1-119-97639-4(hardback)
1. Multiplecriteriadecisionmaking.2. Artificialintelligence. I. Grigoroudis,Evangelos. II. Title.
T57.95.D5782013
658.4’033–dc23
2012040171
AcataloguerecordforthisbookisavailablefromtheBritishLibrary.
ISBN:978-1-119-97639-4
Setin10/12ptTimesbyLaserwordsPrivateLimited,Chennai,India
Contents
Preface xi
Notes on Contributors xv
Part I THECONTRIBUTIONSOFINTELLIGENTTECHNIQUESIN
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
vi CONTENTS
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 INTELLIGENTTECHNOLOGIESFORDECISIONSUPPORT
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 trackof 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 Borra`s
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
CONTENTS vii
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 Szela¸g, Salvatore Greco and Roman Słowin´ski
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
viii CONTENTS
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 Lo´pez 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
CONTENTS ix
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 Sarı, 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
x CONTENTS
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
Preface
Intherapidlyevolvingtechnologicalandbusinessenvironment,decisionmakingbecomes
increasingly more complex from many perspectives. For instance, environmental and
sustainable development issues have risen but the related policies, priorities, goals, and
socio-economic tradeoffs are not well defined or understood in depth. Furthermore, tech-
nological advances provide new capabilities in many areas such as telecommunications,
web-basedtechnologies,transportationandlogistics,manufacturing,energymanagement,
and worldwide trade. Finally, the economic turmoil increases the uncertainties in the
global business environment, and has direct impact on all socio-economic and techno-
logical policies.
Such a challenging environment calls for the implementation of enhanced tools, pro-
cesses, and techniques for decision analysis and support. Clearly, these should take into
account the aforementioned multiple diverse aspects, in combination with the specific
priorities and goals set by decision and policy makers. This has always been a prerequi-
siteforprovidingdecisionsupportinarealisticcontext.Butitisnotenoughanymore,as
decisionsupporttechnologiesshouldnowadaysalsoaccommodateavarietyofnew(often
crucial) requirements, such as distributed decision making, the handling of massive and
increasingly complex data and structures, as well as the computational difficulties that
arise in building and using models and systems, which are realistic enough to represent
the dynamic nature of existing problems and challenges posed by new ones.
Multicriteria decision aid (MCDA) has evolved significantly over the past decades
as a major discipline in operations research, dealing with providing decision support
in complex, ill-structured problems involving multiple (conflicting) criteria. MCDA is
involved in all aspects of the decision process, including problem structuring, model
building, formulation of recommendations, implementation and support. Issues like pref-
erence modeling, the construction of proper sets of criteria and their measurement, the
characterization of different criteria aggregation models, the development of effective
interactive solution techniques, and the implementation of sophisticated methods in user-
friendlydecisionsupportsystems,havetraditionallybeenatthecoreofMCDAresearch.
Among the many exciting trends developing in the area of MCDA, the one focused
on exploring the connections of MCDA with other disciplines, is particularly interest-
ing for providing integrated decision support in the complex context described above.
In this framework, artificial intelligence (AI) has attracted much interest. Nowadays, AI
is a broad field within which one can identify several major research areas, including