[1]JIN Wei,QIAN Jin,YU Ying,et al.Research on TOPSIS decision-making method based on multi-granularity hesitant fuzzy linguistic term sets[J].CAAI Transactions on Intelligent Systems,2024,19(4):1052-1060.[doi:10.11992/tis.202306015]
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Research on TOPSIS decision-making method based on multi-granularity hesitant fuzzy linguistic term sets

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 1052-1060 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2024-07-05
Title:
Research on TOPSIS decision-making method based on multi-granularity hesitant fuzzy linguistic term sets
Author(s):
JIN Wei1;  QIAN Jin1;  YU Ying1;  MIAO Duoqian1; 2
1. School of Software, East China Jiaotong University, Nanchang 330013, China;
2. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Keywords:
multi-granularity; multi-attribute decision; hesitant and fuzzy set; linguistic term set; ambiguous linguistic; decision model; TOPSIS method; optimal solution selection
CLC:
TP391
DOI:
10.11992/tis.202306015
Abstract:
In order to solve the problem that decision makers adopt different granularity linguistic term sets for expression and thus lead to inaccurate decision results due to different knowledge backgrounds in practical decision making, this paper proposes a technique for order preference by similarity to ideal solution(TOPSIS) decision-making method based on multi-granularity hesitant fuzzy linguistic term sets. Firstly, the maximum granularity of each term set is selected as the standard granularity, and the linguistic term set of each decision maker is converted to the same standard granularity for clustering through the conversion algorithm, which results in corresponding subordination linguistic term set; Then, combining with TOPSIS, the distance between each alternative and the positive and negative ideal points is calculated, and the selection of the optimal solution is realized by the ordering of the magnitude of relative closeness; Finally, the feasibility and superiority of the method are verified by an example. The method proposed in this paper can be applied to the problem of choosing the optimal solution to improve the accuracy of decision-making results.
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