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Article type: Research Article
Authors: Sun, Ganga | Wang, Mingxina | Li, Xiaopingb; * | Huang, Weia
Affiliations: [a] School of Science, Hunan Institute of Technology, Hengyang, China | [b] School of Management, Tianjin Normal University, Tianjin, China
Correspondence: [*] Corresponding author. Xiaoping Li, School of Management, Tianjin Normal University, Tianjin 300387. E-mail: lxpmath@126.com.
Note: [*] This work has been supported in part by National Natural Science Foundation of China (Grant No. 61463019), by Natural Science Foundation of Hunan Province (Grant No. 2019JJ40062), and by Research Foundation of Education Bureau of Hunan Province (Grant Nos. 20C0565, 21C0806, S202211528069).
Abstract: In real life, people often need to aggregate some multi criteria fuzzy information and then make reasonable and effective decisions. The distance measure in intuitionistic fuzzy set (IFS) space is an important tool to deal with multi criteria information fuzzy decision making problems. Motivated by these reasons, an intuitionistic fuzzy TOPSIS multi criteria decision-making method is proposed based on distance measure represented by centroid coordinates. Firstly, some existing distance measures in IFS space are summarized, and some of existing shortcomings are discussed. Secondly, the concept of hesitation factor is proposed by using the centroid coordinate representation of hesitation region, and then a new distance measure between two intuitionistic fuzzy numbers is defined. It is proved that the distance measure satisfies the traditional distance axioms. Then, an intuitionistic fuzzy TOPSIS method based on the proposed distance measure is developed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method. Also, the superiority and advantages of the method are shown via comparative analysis and discussion.
Keywords: Intuitionistic fuzzy set (IFS), centroid coordinate representation, hesitation factor, distance measure, TOPSIS method
DOI: 10.3233/JIFS-221732
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 555-571, 2023
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