New concepts and trends of hybrid multiple criteria decision making /
"When humans or computers need to make a decision, typically multiple conflicting criteria need to be evaluated--such as when we buy a car, we need to consider safety, cost and comfort. Multiple-Criteria Decision-Making has been researched for decades. Now as the rising trend of big-data analyt...
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| Format: | eBook |
| Language: | English |
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New York :
CRC Press,
2017.
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| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Cover; Half Title; Title Page; Copyright Page; Table of Contents; Preface; Authors; 1: Introduction; 1.1 Overview of Traditional MCDM Techniques and Methods; 1.2 Statistics versus MCDM Approach; 1.3 History of MADM; 1.4 History of MODM; 1.5 Developments in Computational Intelligence, Machine Learning, and Soft Computing for Decision Aids ; 1.5.1 Basic Concepts of Fuzzy Sets; 1.5.2 Basic Notions of Rough Sets; 1.6 Emerging Trend in Multiple Rule-Based Decision Making; 1.7 Outline of the Book; I: Concepts and Theory; 2: New Concepts and Trends in MCDM; 2.1 Problem Solving in Traditional MCDM.
- 2.2 Why New Hybrid MCDM Approaches Are Needed2.3 Framework of New Hybrid MCDM Models for Tomorrow; 3: Basic Concepts of DEMATEL and Its Revision; 3.1 Background and Basic Notions of DEMATEL; 3.2 Operational Steps of the Original DEMATEL; 3.3 Infeasibility of the Original DEMATEL Technique; 3.4 Revised DEMATEL; 3.5 Two Numerical Examples ; 3.6 Conclusion ; 4: DEMATEL Technique for Forming INRM and DANP Weights; 4.1 Methodology for Assessing Real-World Problems; 4.2 Constructing an Influential Network Relations Map; 4.3 Determining Influential Weights Using DANP.
- 4.4 Problem Solving for Ranking or Selection Decision by INRM and DANP4.5 Conclusion; 5: Traditional MADM and New Hybrid MADM for Problem Solving; 5.1 Traditional MADM for Ranking and Selection; 5.1.1 AHP and ANP; 5.2 New Hybrid Modified MADM; 5.2.1 DEMATEL-Based ANP Instead of AHP and ANP; 5.2.2 Modified VIKOR for Measuring Performance Gaps; 5.3 Additive and Nonadditive Types of Aggregators; 5.3.1 Additive-Type Aggregators; 5.3.2 Nonadditive-Type Aggregators (Fuzzy Integrals); 6: MODM with De Novo and Changeable Spaces; 6.1 Basic Concepts and Trends of MODM; 6.2 De Novo Programming.
- 6.3 MOP with Changeable Parameters6.4 Discussion; 6.5 Conclusion; 7: Multiple Rules-Based Decision Making for Solving Data-Centric Problems; 7.1 Variable-Consistency Dominance-Based Rough Set Approach ; 7.2 Basic Notions of the Reference Point-Based MRDM Approach; 7.3 Core Attribute-Based MRDM Approach; 7.4 Hybrid Bipolar MRDM Approach; 7.4.1 Dominance-Based Rough Set Approach; 7.4.2 Evaluations for an Aggregated Bipolar Decision Model; II: Applications of MCDM; 8: The Case of DEMATEL for Assessing Information Risk; 8.1 Background of the Case and the Research Framework.
- 8.2 DEMATEL Analysis with INRM8.3 DANP Influential Weights for Criteria; 8.4 Discussion and Conclusion; 9: E-Store Business Evaluation and Improvement Using a Hybrid MADM Model; 9.1 Background of the Case and the Research Framework; 9.2 DANP for Finding Influential Weights; 9.3 Performance Measures and Modified VIKOR for Evaluations; 9.4 Discussion; 9.5 Conclusion; 10: Improving the Performance of Green Suppliers in the TFT-LCD Industry; 10.1 Background of the Case; 10.2 Research Framework and the Selected Criteria; 10.3 DANP for Finding Influential Weights of Criteria.