发帖
雷达卡
Author(s): Mohamed El Alaoui
Description:
Chapter 1 distinguishes the causes, sources, and types of uncertainty. To which we have to add, according to Zimmermann, the context, kind of available inputs, desired outputs, and inference mechanism used in order to choose the appropriate model to represent uncertainty. Then, without claiming to provide the magic tool, fuzzy logic is reviewed as a logic in Chapter 2, completing and extending classical and multivalued logics, and as a tool to model uncertainty that fits numerous situations described in the first part. The chapter also discusses other unconventional logics, such as quantum theory, and the reasons for their rejection. Chapter 3 reviews the required mathematical tools, differentiates fuzzy sets from classical sets, and discusses some extensions of fuzzy sets. Based on a general decision-making scheme, and the absence of an absolute best method, Chapter 4 is devoted to frequently used decision-making methods (SAW, AHP, ELECTRE, PROMETHEE, and VIKOR).
Chapter 5 starts from the initial methodology of TOPSIS with a relevant MATLAB® algorithm. It reviews the main causes of rank reversal, namely ideal solutions, and normalization, in addition to their combined effect. The chapter also highlights rank reversal in other methods and discusses the impact of the distance used on the final ranking of alternatives.
To capture both quantitative and qualitative criteria, TOPSIS has been extended using fuzzy logic. Chapter 6 reviews some extensions with relevant examples, in addition to a corresponding MATLAB algorithm. Qualitative criteria, as opposed to quantitative ones, depend heavily on the person making the assessments. This is why they require several decision makers to reduce bias. To merge these opinions into a consensual one, a consensus-reaching method is integrated. The chapter also discusses some frequently used algorithm parameters, including aggregation, distance, normalization, and the choice of ideal solutions. Furthermore, it presents TOPSIS adaptations to different types of fuzzy sets.
To adapt to the intuitionistic fuzzy context, Chapter 7 presents aggregation as a part of the information-integration process, discussing the classically used forms in the intuitionistic fuzzy context, and improved ones. It also presents the adapted distances and consensus-reaching process, the latter of which permits a better optimized function with fewer operations. Then a brief review of some intuitionistic fuzzy TOPSIS approaches is presented. After that, the proposed approach for a consensual TOPSIS approach (with examples) is extended for interval-valued and continuous intuitionistic fuzzy sets using trapezoidal representation. The last section discusses a method to derive attribute weights in intuitionistic fuzzy TOPSIS when they are unknown.
Chapter 8 reviews emerging fuzzy TOPSIS approaches using neutrosophic sets, hesitant fuzzy sets, and Pythagorean fuzzy sets.
Fuzzy Topsis_ Logic, Approaches, and Case Studies- (2021).pdf
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