Title:
Multi-criteria decision analysis : methods and software
Personal Author:
Publication Information:
Chichester, West Sussex, United Kingdom : Wiley, 2013
Physical Description:
ix, 296 p. : ill. ; 23 cm.
ISBN:
9781119974079
Added Author:
Added Corporate Author:
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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010324908 | HD30.213 I84 2013 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Author Notes
Alessio Ishizaka
Reader in Decision Analysis, Portsmouth Business School, University of Portsmouth, UK
Philippe Nemery
Senior Research Scientist, SAP Labs - China, Shanghai, PRC
Table of Contents
Foreword | p. xi |
Acknowledgements | p. xiii |
1 General introduction | p. 1 |
1.1 Introduction | p. 1 |
1.2 Decision problems | p. 3 |
1.3 MCDA methods | p. 4 |
1.4 MCDA software | p. 5 |
1.5 Selection of MCDA methods | p. 5 |
1.6 Outline of the book | p. 8 |
References | p. 9 |
Part I Full Aggregation Approach | p. 11 |
2 Analytic hierarchy process | p. 13 |
2.1 Introduction | p. 13 |
2.2 Essential concepts of AHP | p. 13 |
2.2.1 Problem structuring | p. 14 |
2.2.2 Priority calculation | p. 16 |
2.2.3 Consistency check | p. 18 |
2.2.4 Sensitivity analysis | p. 19 |
2.3 AHP software: MakeItRational | p. 20 |
2.3.1 Problem structuring | p. 20 |
2.3.2 Preferences and priority calculation | p. 21 |
2.3.3 Consistency check | p. 22 |
2.3.4 Results | p. 24 |
2.3.5 Sensitivity analysis | p. 25 |
2.4 In the black box of AHP | p. 27 |
2.4.1 Problem structuring | p. 27 |
2.4.2 Judgement scales | p. 28 |
2.4.3 Consistency | p. 31 |
2.4.4 Priorities derivation | p. 33 |
2.4.5 Aggregation | p. 39 |
2.5 Extensions of AHP | p. 40 |
2.5.1 Analytic hierarchy process ordering | p. 41 |
2.5.2 Group analytic hierarchy process | p. 44 |
2.5.3 Clusters and pivots for a large number of alternatives | p. 48 |
2.5.4 AHPSort | p. 50 |
References | p. 54 |
3 Analytic network process | p. 59 |
3.1 Introduction | p. 59 |
3.2 Essential concepts of ANP | p. 59 |
3.2.1 Inner dependency in the criteria cluster | p. 60 |
3.2.2 Inner dependency in the alternative cluster | p. 63 |
3.2.3 Outer dependency | p. 64 |
3.2.4 Influence matrix | p. 67 |
3.3 ANP software: Super Decisions | p. 68 |
3.3.1 Problem structuring | p. 69 |
3.3.2 Assessment of pairwise comparison | p. 70 |
3.3.3 Results | p. 73 |
3.3.4 Sensitivity analysis | p. 74 |
3.4 In the black box of ANP | p. 76 |
3.4.1 Markov chain | p. 76 |
3.4.2 Supermatrix | p. 78 |
References | p. 80 |
4 Multi-attribute utility theory | p. 81 |
4.1 Introduction | p. 81 |
4.2 Essential concepts of MAUT | p. 81 |
4.2.1 The additive model | p. 83 |
4.3 Right Choice | p. 89 |
4.3.1 Data input and utility functions | p. 89 |
4.3.2 Results | p. 93 |
4.3.3 Sensitivity analysis | p. 94 |
4.3.4 Group decision and multi-scenario analysis | p. 95 |
4.4 In the black box of MAUT | p. 97 |
4.5 Extensions of the MAUT method | p. 98 |
4.5.1 The UTA method | p. 98 |
4.5.2 UTA GMS | p. 105 |
4.5.3 GRIP | p. 111 |
References | p. 112 |
5 MACBETH | p. 114 |
5.1 Introduction | p. 114 |
5.2 Essential concepts of MACBETH | p. 114 |
5.2.1 Problem structuring: Value tree | p. 115 |
5.2.2 Score calculation | p. 117 |
5.2.3 Incompatibility check | p. 118 |
5.3 Software description: M-MACBETH | p. 122 |
5.3.1 Problem structuring: Value tree | p. 122 |
5.3.2 Evaluations and scores | p. 122 |
5.3.3 Incompatibility check | p. 125 |
5.3.4 Results | p. 127 |
5.3.5 Sensitivity analysis | p. 127 |
5.3.6 Robustness analysis | p. 127 |
5.4 In the black box of MACBETH | p. 131 |
5.4.1 LP-MACBETH | p. 131 |
5.4.2 Discussion | p. 133 |
References | p. 133 |
Part II Outranking Approach | p. 135 |
6 Promethee | p. 137 |
6.1 Introduction | p. 137 |
6.2 Essential concepts of the PROMETHEE method | p. 137 |
6.2.1 Unicriterion preference degrees | p. 138 |
6.2.2 Unicriterion positive, negative and net flows | p. 142 |
6.2.3 Global flows | p. 143 |
6.2.4 The Gaia plane | p. 146 |
6.2.5 Sensitivity analysis | p. 148 |
6.3 The Smart Picker Pro software | p. 149 |
6.3.1 Data entry | p. 149 |
6.3.2 Entering preference parameters | p. 151 |
6.3.3 Weights | p. 153 |
6.3.4 PROMETHEE II ranking | p. 155 |
6.3.5 Gaia plane | p. 151 |
6.3.6 Sensitivity analysis | p. 158 |
6.4 In the black box of PROMETHEE | p. 160 |
6.4.1 Unicriterion preference degrees | p. 162 |
6.4.2 Global preference degree | p. 163 |
6.4.3 Global flows | p. 164 |
6.4.4 PROMETHEE I and PROMETHEE II ranking | p. 166 |
6.4.5 The Gaia plane | p. 167 |
6.4.6 Influence of pairwise comparisons | p. 168 |
6.5 Extensions of PROMETHEE | p. 170 |
6.5.1 PROMETHEE GDSS | p. 170 |
6.5.2 FlowSort: A sorting or supervised classification method | p. 172 |
References | p. 177 |
7 ELECTRE | p. 180 |
7.1 Introduction | p. 180 |
7.2 Essentials of the ELECTEE methods | p. 180 |
7.2.1 ELECTRE III | p. 183 |
7.3 The Electre III-IV software | p. 189 |
7.3.1 Data entry | p. 190 |
7.3.2 Entering preference parameters | p. 191 |
7.3.3 Results | p. 193 |
7.4 In the black box of ELECTRE III | p. 194 |
7.4.1 Outranking relations | p. 194 |
7.4.2 Partial concordance degree | p. 195 |
7.4.3 Global concordance degree | p. 196 |
7.4.4 Partial discordance degree | p. 196 |
7.4.5 Outranking degree | p. 197 |
7.4.6 Partial ranking: Exploitation of the outranking relations | p. 199 |
7.4.7 Some properties | p. 203 |
7.5 ELECTRE-Tri | p. 204 |
7.5.1 Introduction | p. 204 |
7.5.2 Preference relations | p. 205 |
7.5.3 Assignment rules | p. 207 |
7.5.4 Properties | p. 207 |
References | p. 210 |
Part III Goal, Aspiration or Reference-Level Approach | p. 213 |
8 TOPSIS | p. 215 |
8.1 Introduction | p. 215 |
8.2 Essentials of TOPSIS | p. 215 |
References | p. 221 |
9 Goal programming | p. 222 |
9.1 Introduction | p. 222 |
9.2 Essential concepts of goal programming | p. 222 |
9.3 Software description | p. 227 |
9.3.1 Microsoft Excel Solver | p. 227 |
9.4 Extensions of the goal programming | p. 228 |
9.4.1 Weighted goal programming | p. 228 |
9.4.2 Lexicographic goal programming | p. 230 |
9.4.3 Chebyshev goal programming | p. 232 |
References | p. 234 |
10 Data Envelopment Analysis | p. 235 |
10.1 Introduction | p. 235 |
10.2 Essential concepts of DEA | p. 236 |
10.2.1 An efficiency measurement method | p. 236 |
10.2.2 A DEA case study | p. 237 |
10.2.3 Multiple outputs and inputs | p. 247 |
10.2.4 Types of efficiency | p. 248 |
10.2.5 Managerial implications | p. 249 |
10.3 The DEA software | p. 252 |
10.3.1 Building a spreadsheet in Win4DEAP | p. 254 |
10.3.2 Running a DEA model | p. 255 |
10.3.3 Interpreting results | p. 257 |
10.4 In the black box of DEA | p. 262 |
10.4.1 Constant returns to scale | p. 263 |
10.4.2 Variable returns to scale | p. 266 |
10.5 Extensions of DEA | p. 268 |
10.5.1 Adjusting for the environment | p. 268 |
10.5.2 Preferences | p. 268 |
10.5.3 Sensitivity analysis | p. 269 |
10.5.4 Time series data | p. 270 |
References | p. 270 |
Part IV Integrated Systems | p. 275 |
11 Multi-method platforms | p. 277 |
11.1 Introduction | p. 277 |
11.2 Decision Deck | p. 278 |
11.3 DECERNS | p. 278 |
11.3.1 The GIS module | p. 279 |
11.3.2 The MCDA module | p. 281 |
11.3.3 The GDSS module | p. 284 |
11.3.4 Integration | p. 286 |
References | p. 287 |
Appendix: Linear optimization | p. 288 |
A.1 Problem modelling | p. 288 |
A.2 Graphical solution | p. 289 |
A.3 Solution with Microsoft Excel | p. 289 |
Index | p. 293 |