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Article type: Research Article
Authors: Wu, Meiqin | Yang, Jindou | Fan, Jianping; *
Affiliations: School of Economics and Management, Shanxi University, Tai yuan, China
Correspondence: [*] Corresponding author. Jianping Fan, School of Economics and Management, Shanxi University, Tai yuan, China. E-mail: fjp@sxu.edu.cn.
Abstract: With the continuous improvement and development of various decision-making methods, it has led to the widespread use of fuzzy sets and fuzzy numbers. At the same time, the application of decision-making methods in different fuzzy environments has been very effective in addressing the deficiencies in existing research. At present, triangular fuzzy numbers have been widely used in the evaluation aspects of various decision making methods, and the proposed R-number effectively solve the uncertainty involving problems related to future events, but the existing research based on the TOPSIS method in the R-number environment has not yet been clearly applied to the triangular fuzzy number environment, and the indifference threshold-based attribute ratio analysis (ITARA) method in the fuzzy environment has yet to be extended. Therefore, this paper proposes a fuzzy indifference threshold-based attribute ratio analysis (FITARA) method based on triangular fuzzy numbers for solving the problem of determining attribute weights in the multi-attribute decision-making process. Secondly, the various risks of the decision environment and the impact on future events are considered and R-number are used to solve this puzzle. In addition, the incorporation of risk perception factors in the context of the existing RTOPSIS method considering multiple risk factors and the use of Manhattan distances to optimize the large number of operations in the process of the method resulted in the development of the FITARA-RTOPSIS model. Finally, the proposed FITARA-RTOPSIS method is applied to the problem of siting emergency supplies storage depots, and the effectiveness of the proposed method is verified through comparative analysis.
Keywords: FITARA, R-number, RTOPSIS, Manhattan distance, TFN
DOI: 10.3233/JIFS-232393
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8889-8905, 2023
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