New concepts and trends of hybrid multiple criteria decision making

363 Pages·2017·5.611 MB·English

by  Tzeng, Gwo-Hshiung

New Concepts and Trends
of Hybrid Multiple Criteria
Decision Making

New Concepts and Trends
of Hybrid Multiple Criteria
Decision Making

By
Gwo-Hshiung Tzeng and Kao-Yi Shen
CRC Press
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Library of Congress Cataloging- in- Publication Data
Names: Tzeng, Gwo-Hshiung, author.
Title: New concepts and trends of hybrid multiple criteria decision making /
Gwo-Hshiung Tzeng and Kao-Yi Shen.
Description: New York : CRC Press, 2017.
Identifiers: LCCN 2017000939 | ISBN 9781498777087 (hbk : alk. paper)
Subjects: LCSH: Multiple criteria decision making. | Decision making. |
Problem solving.
Classification: LCC T57.95 .T94 2017 | DDC 658.4/03--dc23
LC record available at https://lccn.loc.gov/2017000939
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Contents
Preface ...............................................................................................................xi
Authors ...........................................................................................................xiii
1 Introduction ..........................................................................................1
1.1 Overview of Traditional MCDM Techniques and Methods ............1
1.2 Statistics versus MCDM Approach ..................................................4
1.3 History of MADM ..........................................................................5
1.4 History of MODM ..........................................................................8
1.5 Developments in Computational Intelligence, Machine
Learning, and Soft Computing for Decision Aids ..........................10
1.5.1 Basic Concepts of Fuzzy Sets .............................................13
1.5.2 Basic Notions of Rough Sets ..............................................14
1.6 Emerging Trend in Multiple Rule-Based Decision Making ............16
1.7 Outline of the Book ........................................................................19
SeCtion i ConCePtS AnD tHeoRY
2 New Concepts and Trends in MCDM .................................................23
2.1 Problem Solving in Traditional MCDM .........................................25
2.2 Why New Hybrid MCDM Approaches Are Needed .....................26
2.3 Framework of New Hybrid MCDM Models for Tomorrow ..........28
3 Basic Concepts of DEMATEL and Its Revision ..................................35
3.1 Background and Basic Notions of DEMATEL .............................36
3.2 Operational Steps of the Original DEMATEL ..............................36
3.3 Infeasibility of the Original DEMATEL Technique ......................39
3.4 Revised DEMATEL ......................................................................40
3.5 Two Numerical Examples ..............................................................41
3.6 Conclusion .....................................................................................47
4 DEMATEL Technique for Forming INRM and DANP Weights ........49
4.1 Methodology for Assessing Real-World Problems ..........................49
4.2 Constructing an Influential Network Relations Map ....................50
4.3 Determining Influential Weights Using DANP .............................55
v
vi ◾ Contents
4.4 Problem Solving for Ranking or Selection Decision by INRM
and DANP .....................................................................................57
4.5 Conclusion ....................................................................................58
5 Traditional MADM and New Hybrid MADM for Problem Solving ......61
5.1 Traditional MADM for Ranking and Selection .............................61
5.1.1 AHP and ANP ..................................................................62
5.2 New Hybrid Modified MADM .....................................................63
5.2.1 DEMATEL-Based ANP Instead of AHP and ANP..........64
5.2.2 Modified VIKOR for Measuring Performance Gaps ..........65
5.3 Additive and Nonadditive Types of Aggregators ............................68
5.3.1 Additive-Type Aggregators ................................................68
5.3.2 Nonadditive-Type Aggregators (Fuzzy Integrals) ...............68
5.4 Conclusion ....................................................................................72
6 MODM with De Novo and Changeable Spaces ..................................73
6.1 Basic Concepts and Trends of MODM ..........................................74
6.2 De Novo Programming .................................................................78
6.3 MOP with Changeable Parameters ................................................83
6.4 Discussion .....................................................................................89
6.5 Conclusion .....................................................................................91
7 Multiple Rules‑Based Decision Making for Solving Data‑Centric
Problems ..............................................................................................93
7.1 Variable-Consistency Dominance-Based Rough Set Approach .....94
7.2 Basic Notions of the Reference Point-Based MRDM Approach ....96
7.3 Core Attribute-Based MRDM Approach ....................................100
7.4 Hybrid Bipolar MRDM Approach ..............................................100
7.4.1 Dominance-Based Rough Set Approach ..........................101
7.4.2 Evaluations for an Aggregated Bipolar Decision Model ......104
7.5 Conclusion ...................................................................................105
SeCtion ii APPLiCAtionS oF MCDM
8 The Case of DEMATEL for Assessing Information Risk ..................109
8.1 Background of the Case and the Research Framework .................110
8.2 DEMATEL Analysis with INRM ................................................113
8.3 DANP Influential Weights for Criteria .........................................114
8.4 Discussion and Conclusion ...........................................................117
9 E‑Store Business Evaluation and Improvement Using a Hybrid
MADM Model ...................................................................................119
9.1 Background of the Case and the Research Framework ................120
9.2 DANP for Finding Influential Weights .......................................122
Contents ◾ vii
9.3 Performance Measures and Modified VIKOR for Evaluations ....126
9.4 Discussion ...................................................................................126
9.5 Conclusion ...................................................................................132
10 Improving the Performance of Green Suppliers in the TFT‑LCD
Industry .............................................................................................133
10.1 Background of the Case ...............................................................134
10.2 Research Framework and the Selected Criteria .............................135
10.3 DANP for Finding Influential Weights of Criteria ......................136
10.4 Modified PROMETHEE .............................................................137
10.5 Discussion ....................................................................................147
10.6 Conclusion ...................................................................................149
11 Exploring Smartphone Improvements Based on a Hybrid MADM
Model 151
11.1 Background of the Case ................................................................152
11.2 Research Framework ...................................................................152
11.3 DEMATEL Analysis and DANP Influential Weights for
Criteria .......................................................................................156
11.4 Modified VIKOR for Performance Gap Aggregation ...................161
11.5 Discussion on the Improvements of Smartphone Vendors ............165
11.6 Conclusion ...................................................................................166
12 Evaluating the Development of Business‑to‑Business
M‑Commerce of SMEs ......................................................................169
12.1 Research Background ...................................................................169
12.2 Research Framework ....................................................................170
12.2.1 Technological Environment Aspect ..................................170
12.2.2 Organizational Environment Aspect ................................171
12.2.3 External Environment Aspect ..........................................171
12.3 DEMATEL Analysis and DANP Influential Weights for
Criteria .......................................................................................172
12.4 Modified VIKOR for Performance Gap Aggregation ...................174
12.5 Discussion ....................................................................................177
12.6 Conclusion ...................................................................................181
13 Evaluation and Selection of Glamour Stocks by a Hybrid MADM
Model ................................................................................................183
13.1 Research Background and Investment Strategy ............................184
13.2 Research Framework for the G-Score and Hybrid MADM
Models ..........................................................................................184
13.3 DEMATEL Analysis and DANP Influential Weights of the
G-Score Model .............................................................................186
viii ◾ Contents
13.4 Modified VIKOR for Performance Gap Aggregation and
Evaluation ....................................................................................192
13.5 Discussion and Examination of Stock Returns .............................195
13.6 Conclusion ...................................................................................198
14 Nonadditive Hybrid MADM Model for Selecting and Improving
Suppliers ............................................................................................201
14.1 Research Background and Literature Review ...............................202
14.1.1 Multiple Attribute Decision Making ...............................203
14.1.2 Mathematical Programming Models ..............................203
14.1.3 Combined and Integrated Hybrid Approaches ................203
14.3 Hybrid MADM Model Using Nonadditive-Type Aggregators.....204
14.3.1 Determining Gap Values Based on the New Concepts
of Modified VIKOR Method ..........................................205
14.3.2 Applying λ Fuzzy Measures for Fuzzy Integrals ..............206
14.4 Numerical Example .....................................................................207
14.4.1 Measuring the Influential Relations and Weights by
DEMATEL and DANP ..................................................208
14.4.2 Integrated Weighted Gaps Using the Fuzzy Integral
Technique .......................................................................208
14.5 Discussion on Improving toward the Aspired Levels ....................216
14.6 Conclusion ...................................................................................217
15 New Perspectives on Modeling Strategic Alliances by De Novo
Programming ....................................................................................219
15.1 I ntroduction to Strategic Alliances and Literature Review ............219
15.1.1 Transaction Cost Theory .................................................220
15.1.2 Resource-Dependent Theory ...........................................220
15.1.3 Strategic Behavior and Organizational Learning
Perspectives ......................................................................221
15.2 Resource Allocation and De Novo Programming Perspectives .....221
15.3 Numerical Example .....................................................................224
15.4 Discussion ...................................................................................226
15.5 Conclusion ..................................................................................227
16 Automated Factory Planning Using the New Idea of Changeable
Spaces ................................................................................................229
16.1 Background of the Case ...............................................................230
16.2 Research Framework and the Evolution of Optimization .............231
16.2.1 Conventional Pareto Solution .........................................232
16.2.2 De Novo Programming for Optimization .......................232
16.2.3 New Ideas of Changeable Spaces......................................233
16.3 Discussion ....................................................................................235
16.4 Conclusion ..................................................................................236
Contents ◾ ix
17 Fuzzy Inference‑Supported MRDM for Technical Analysis: A
Case of Stock Investment ..................................................................239
17.1 B ackground of Technical Analysis and Computational
Intelligence Techniques ...............................................................240
17.2 H ybrid Investment Support System Based on Fuzzy and
Rough Set Techniques .................................................................242
17.3 Numerical Experiments ...............................................................244
17.3.1 Data Preprocessing ..........................................................244
17.3.2 Discretization of Fuzzy TA Signals by Fuzzy Inference
Systems ............................................................................249
17.3.3 VC-DRSA Model ............................................................250
17.4 Simulated Investment Performance and Discussion ......................252
17.5 Conclusion ..................................................................................254
18 Financial Improvements of Commercial Banks Using a Hybrid
MRDM Approach .............................................................................255
18.1 Research Background ...................................................................255
18.2 C ore Attributes–Based MRDM Approach for Financial
Performance Improvement ...........................................................257
18.3 An Empirical Case of Five Commercial Banks .............................259
18.4 Analytical Results by the Modified VIKOR Method ..................264
18.5 Discussion ...................................................................................268
18.6 Conclusion ...................................................................................270
19 FCA‑Based DANP Model Using the Rough Set Approach: A Case
Study of Semiconductor Companies .................................................273
19.1 Research Background ..................................................................273
19.2 R eviews of MCDM and Soft Computing Methods in
Financial Applications ..................................................................275
19.3 Framework of the Hybrid MRDM Model ...................................278
19.4 Case Study of Semiconductor Companies ...................................280
19.5 FCA-Based DANP Model for Ranking Improvement Plans ........284
19.6 Discussion on Improvement Planning .........................................286
19.7 Conclusion ..................................................................................288
20 Hybrid Bipolar MRDM Model For Business Analytics ...................289
20.1 Background of Business Analytics ..............................................290
20.2 Hybrid MRDM Model Using the Bipolar Approach ....................291
20.3 A Case from the Semiconductor Industry ....................................298
20.3.1 Data ................................................................................298
20.3.2 Bipolar Weighting System ...............................................299
20.3.3 Aggregate Fuzzy Performance Evaluations Using
Modified VIKOR ...........................................................302