Entropy based extended TOPOSIS method for MCDM problem with fuzzy credibility numbers

• Published in: AIMS mathematics Vol. 7; no. 9; pp. 17286 - 17312
• Main Authors: Midrar, Talha, Khan, Saifullah, Abdullah, Saleem, Botmart, Thongchai
• Format: Journal Article
• Language: English
• Published: AIMS Press 01.01.2022
• Subjects: aggregation operators; decision-making; dombi operational laws for fcns; fuzzy credibility numbers; topsis method
• ISSN: 2473-6988; 2473-6988
• DOI: 10.3934/math.2022952

Due to the vagueness and uncertainty of human cognition/judgments as related to complicated decision-making problems, existing fuzzy decision-making approaches merely signal fuzzy assessment values and lack degrees/levels of credibility for the fuzzy assessment values in alternatives over attributes. As a result, the fuzzy evaluative value's credibility degree highlights its significance and importance in the fuzzy decision-making problem. To improve the degrees/levels of credibility of fuzzy evaluation values, the fuzzy assessment values should be tightly linked to their credibility measures, which would result in more abundant and reliable assessment information. The major goal of this research was to describe new procedures for credible fuzzy numbers based on the Dombi t-norm and Dombi t-conorm. Dombi operations can benefit from the operational parameter's best tractability. These operations are more generalized for credibility fuzzy numbers. Furthermore, using the basic operational laws of Dombi t-norm and Dombi t-conorm, we develop a series of fuzzy credibility Dombi aggregation operators, like the fuzzy credibility Dombi geometric aggregation operator, fuzzy credibility Dombi ordered geometric aggregation operator and fuzzy credibility Dombi hybrid geometric aggregation operator. To handle this sort of decision-making problem, an extended TOPSIS (technique for order of preference by similarity to ideal solution) is proposed. Finally, we present an example, along with a discussion of the comparative results to check the accuracy and validation of the proposed methods, to confirm that their results are credible and feasible.