✍ Scribed by Xiaoli Tian, Zeshui Xu
No coin nor oath required. For personal study only.
This book gives a thorough and systematic introduction to the latest research results about fuzzy decision-making method based on prospect theory. It includes eight chapters: Introduction, Intuitionistic fuzzy MADM based on prospect theory, QUALIFLEX based on prospect theory with probabilistic linguistic information, Group PROMETHEE based on prospect theory with hesitant fuzzy linguistic information, Prospect consensus with probabilistic hesitant fuzzy preference information, Improved TODIM based on prospect theory and the improved TODIM with probabilistic hesitant fuzzy information, etc. This book is suitable for the researchers in the fields of fuzzy mathematics, operations research, behavioral science, management science and engineering, etc. It is also useful as a textbook for postgraduate and senior-year undergraduate students of the relevant professional institutions of higher learning.
Preface
References
Contents
1 Introduction
1.1 Background
1.1.1 Development of Bounded Rationality
1.1.2 Development of Fuzzy Information
1.1.3 Importance of Research About Fuzzy Decision Making with PT
1.2 Corresponding Preliminaries
1.2.1 PT
1.2.2 TODIM
1.2.3 Intuitionistic Fuzzy Information
1.2.4 Probabilistic Hesitant Fuzzy Information
1.2.5 Hesitant Fuzzy Linguistic Information
1.2.6 Probabilistic Linguistic Information
1.3 Aim and Focus of This Book
References
2 Intuitionistic Fuzzy MADM Based on PT
2.1 Decision-Making Procedure
2.2 Illustrative Example
2.2.1 Decision-Making Attributes Used by VCs
2.2.2 Selecting Process and Results Derived by IFPT
2.2.3 Selecting Process and Results Derived by TOPSIS
2.3 Remarks
References
3 QUALIFLEX Based on PT with Probabilistic Linguistic Information
3.1 Procedure of P-QUALIFLEX with Probabilistic Linguistic Information
3.2 Procedure of the Extended QUALIFLEX with Probabilistic Linguistic Information
3.3 Illustrative Example
3.3.1 Results of P-QUALIFLEX with Probabilistic Linguistic Information
3.3.2 Results of the Extended QUALIFLEX with Probabilistic Linguistic Information
3.4 Comparative Analysis
3.4.1 Comparison of P-QUALIFLEX with Extended QUALIFLEX
3.4.2 Comparison of P-QUALIFLEX with TODIM
3.5 Remarks
References
4 Group PROMETHEE Based on PT with Hesitant Fuzzy Linguistic Information
4.1 GP-PROMETHEE with Hesitant Fuzzy Linguistic Information
4.2 G-PROMETHEE with Hesitant Fuzzy Linguistic Information
4.3 Illustrative Example
4.3.1 Decision-Making Background
4.3.2 Results of the GP-PROMETHEE with Hesitant Fuzzy Linguistic Information
4.3.3 Results of the G-PROMETHEE with Hesitant Fuzzy Linguistic Information
4.3.4 Results of TODIM with Hesitant Fuzzy Linguistic Information
4.3.5 Comparative Analysis
4.4 Simulation Analysis
4.5 Remarks
References
5 Prospect Consensus with Probabilistic Hesitant Fuzzy Preference Information
5.1 Probabilistic Hesitant Fuzzy Preference Information
5.2 Consensus Model Based on PT with P-HFPs
5.2.1 Prospect Consensus Measure with P-HFPs
5.2.2 Procedure of Reaching Prospect Consensus and Decision-Making
5.3 Illustrative Example
5.3.1 Sequential Decision-Making Attributes
5.3.2 Results of Prospect Consensus with P-HFPs
5.3.3 Results of the Expected Consensus Process with P-HFPs
5.3.4 Results of Prospect Consensus with HFPs
5.3.5 Results of the Expected Consensus with HFPs
5.3.6 Comparative Analysis
5.4 Simulated Analysis
5.5 Remarks
References
6 An Improved TODIM Based on PT
6.1 Procedure of the Improved TODIM
6.2 Illustrative Example
6.2.1 Decision-Making Background
6.2.2 Results of the Improved TODIM
6.2.3 Results of the Classical TODIM
6.2.4 Comparative Analysis Between the Improved and the Classical TODIM
6.3 Remarks
References
7 An Improved TODIM with Probabilistic Hesitant Fuzzy Information
7.1 Procedure of the Improved TODIM with Probabilistic Hesitant Fuzzy Information
7.2 Procedure of the Improved TODIM with Hesitant Fuzzy Information
7.3 Illustrative Analysis
7.3.1 Screening Process of the Improved TODIM with Probabilistic Hesitant Fuzzy Information
7.3.2 Screening Process of the Extended TODIM with Probabilistic Hesitant Fuzzy Information
7.3.3 Screening Process of the Improved TODIM with Hesitant Fuzzy Information
7.3.4 Screening Process of the Extended TODIM with Hesitant Fuzzy Information
7.3.5 Analysis
7.4 Comparative Analysis
7.4.1 Comparative Analysis with the TOPSIS Method
7.4.2 Sensitivity Analysis Based on the Parameter Values
7.5 Simulation Analysis
7.6 Remarks
References
8 Conclusions
8.1 Summary
8.2 Future Studies
Reference
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