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This book presents an introduction to MCDA followed by more detailed chapters about each of the leading methods used in this field. Comparison of methods and software is also featured to enable readers to choose the most appropriate method needed in their research.
Worked examples as well as the software featured in the book are available on an accompanying website.
Foreword xi
Acknowledgements xiii
1 General introduction 1
1.1 Introduction 1
1.2 Decision problems 3
1.3 MCDA methods 4
1.4 MCDA software 5
1.5 Selection of MCDA methods 5
1.6 Outline of the book 8
References 9
Part I FULL AGGREGATION APPROACH 11
2 Analytic hierarchy process 13
2.1 Introduction 13
2.2 Essential concepts of AHP 13
2.2.1 Problem structuring 14
2.2.2 Priority calculation 16
2.2.3 Consistency check 18
2.2.4 Sensitivity analysis 19
2.3 AHP software: MakeItRational 20
2.3.1 Problem structuring 20
2.3.2 Preferences and priority calculation 21
2.3.3 Consistency check 22
2.3.4 Results 24
2.3.5 Sensitivity analysis 25
2.4 In the black box of AHP 27
2.4.1 Problem structuring 27
2.4.2 Judgement scales 28
2.4.3 Consistency 31
2.4.4 Priorities derivation 33
2.4.5 Aggregation 39
2.5 Extensions of AHP 40
2.5.1 Analytic hierarchy process ordering 41
2.5.2 Group analytic hierarchy process 44
2.5.3 Clusters and pivots for a large number of alternatives 48
2.5.4 AHPSort 50
References 54
3 Analytic network process 59
3.1 Introduction 59
3.2 Essential concepts of ANP 59
3.2.1 Inner dependency in the criteria cluster 60
3.2.2 Inner dependency in the alternative cluster 63
3.2.3 Outer dependency 64
3.2.4 Influence matrix 67
3.3 ANP software: Super Decisions 68
3.3.1 Problem structuring 69
3.3.2 Assessment of pairwise comparison 70
3.3.3 Results 73
3.3.4 Sensitivity analysis 74
3.4 In the black box of ANP 76
3.4.1 Markov chain 76
3.4.2 Supermatrix 78
References 80
4 Multi-attribute utility theory 81
4.1 Introduction 81
4.2 Essential concepts of MAUT 81
4.2.1 The additive model 83
4.3 RightChoice 89
4.3.1 Data input and utility functions 89
4.3.2 Results 93
4.3.3 Sensitivity analysis 94
4.3.4 Group decision and multi-scenario analysis 95
4.4 In the black box of MAUT 97
4.5 Extensions of the MAUT method 98
4.5.1 The UTA method 98
4.5.2 UTAGMS 105
4.5.3 GRIP 111
References 112
5 MACBETH 114
5.1 Introduction 114
5.2 Essential concepts of MACBETH 114
5.2.1 Problem structuring: Value tree 115
5.2.2 Score calculation 117
5.2.3 Incompatibility check 118
5.3 Software description: M-MACBETH 122
5.3.1 Problem structuring: Value tree 122
5.3.2 Evaluations and scores 122
5.3.3 Incompatibility check 125
5.3.4 Results 127
5.3.5 Sensitivity analysis 127
5.3.6 Robustness analysis 127
5.4 In the black box of MACBETH 131
5.4.1 LP-MACBETH 131
5.4.2 Discussion 133
References 133
Part II OUTRANKING APPROACH 135
6 PROMETHEE 137
6.1 Introduction 137
6.2 Essential concepts of the PROMETHEE method 137
6.2.1 Unicriterion preference degrees 138
6.2.2 Unicriterion positive, negative and net flows 142
6.2.3 Global flows 143
6.2.4 The Gaia plane 146
6.2.5 Sensitivity analysis 148
6.3 The Smart Picker Pro software 149
6.3.1 Data entry 149
6.3.2 Entering preference parameters 151
6.3.3 Weights 153
6.3.4 PROMETHEE II ranking 155
6.3.5 Gaia plane 157
6.3.6 Sensitivity analysis 158
6.4 In the black box of PROMETHEE 160
6.4.1 Unicriterion preference degrees 162
6.4.2 Global preference degree 163
6.4.3 Global flows 164
6.4.4 PROMETHEE I and PROMETHEE II ranking 166
6.4.5 The Gaia plane 167
6.4.6 Influence of pairwise comparisons 168
6.5 Extensions of PROMETHEE 170
6.5.1 PROMETHEE GDSS 170
6.5.2 FlowSort: A sorting or supervised classification method 172
References 177
7 ELECTRE 180
7.1 Introduction 180
7.2 Essentials of the ELECTRE methods 180
7.2.1 ELECTRE III 183
7.3 The Electre III-IV software 189
7.3.1 Data entry 190
7.3.2 Entering preference parameters 191
7.3.3 Results 193
7.4 In the black box of ELECTRE III 194
7.4.1 Outranking relations 194
7.4.2 Partial concordance degree 195
7.4.3 Global concordance degree 196
7.4.4 Partial discordance degree 196
7.4.5 Outranking degree 197
7.4.6 Partial ranking: Exploitation of the outranking relations 199
7.4.7 Some properties 203
7.5 ELECTRE-Tri 204
7.5.1 Introduction 204
7.5.2 Preference relations 205
7.5.3 Assignment rules 207
7.5.4 Properties 207
References 210
Part III GOAL, ASPIRATION OR REFERENCE-LEVEL APPROACH 213
8 TOPSIS 215
8.1 Introduction 215
8.2 Essentials of TOPSIS 215
References 221
9 Goal programming 222
9.1 Introduction 222
9.2 Essential concepts of goal programming 222
9.3 Software description 227
9.3.1 Microsoft Excel Solver 227
9.4 Extensions of the goal programming 228
9.4.1 Weighted goal programming 228
9.4.2 Lexicographic goal programming 230
9.4.3 Chebyshev goal programming 232
References 234
10 Data Envelopment Analysis 235
Jean-Marc Huguenin
10.1 Introduction 235
10.2 Essential concepts of DEA 236
10.2.1 An efficiency measurement method 236
10.2.2 A DEA case study 237
10.2.3 Multiple outputs and inputs 247
10.2.4 Types of efficiency 248
10.2.5 Managerial implications 249
10.3 The DEA software 252
10.3.1 Building a spreadsheet in Win4DEAP 254
10.3.2 Running a DEA model 255
10.3.3 Interpreting results 257
10.4 In the black box of DEA 262
10.4.1 Constant returns to scale 263
10.4.2 Variable returns to scale 266
10.5 Extensions of DEA 268
10.5.1 Adjusting for the environment 268
10.5.2 Preferences 268
10.5.3 Sensitivity analysis 269
10.5.4 Time series data 270
References 270
Part IV INTEGRATED SYSTEMS 275
11 Multi-method platforms 277
11.1 Introduction 277
11.2 Decision Deck 278
11.3 DECERNS 278
11.3.1 The GIS module 279
11.3.2 The MCDA module 281
11.3.3 The GDSS module 284
11.3.4 Integration 286
References 287
Appendix: Linear optimization 288
A.1 Problem modelling 288
A.2 Graphical solution 289
A.3 Solution with Microsoft Excel 289
Index 293
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