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Article type: Research Article
Authors: Zhang, Mo | Zhang, Qinghua; * | Gao, Man
Affiliations: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
Correspondence: [*] Corresponding author. Qinghua Zhang, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications. Tel.: +86 023 62471901; E-mail: zhangqh@cqupt.edu.cn.
Abstract: As a new extended model of fuzzy sets, hesitant fuzzy set theory is a useful tool to process uncertain information in decision making problems. The traditional hesitant fuzzy multi-attribute decision making (MADM) can only choose an optimal strategy, which is not suitable for all of the complex scenarios. Typically, in practical application, decision making problems may be more complicated involving three options of acceptance, non-commitment and rejection decisions. Three-way decisions, which divide universe into three disjoint regions by a pair of thresholds, are more efficient to deal with these problems. Therefore, how to utilize three-way decision theory to process hesitant fuzzy information is an essential issue to be studied. In this paper, from the perspective of hesitant fuzzy distance, a hesitant fuzzy three-way decision model is proposed. First, because hesitant fuzzy element (HFE) is a set of several possible membership degrees, it cannot be compared with thresholds directly. Hence, this paper converts it into the comparison between the distance and the thresholds. Then, to calculate thresholds more reasonably, shadowed set theory is introduced to avoid the subjectivity of threshold acquisition. Furthermore, sequential strategy is adopted to solve the multi-attribute decision making problems. Finally, an example of medical diagnosis and simulation experiments are given to prove the accuracy and efficiency of the proposed hesitant fuzzy three-way decision model.
Keywords: Hesitant fuzzy sets, three-way decisions, shadowed sets, sequential strategy
DOI: 10.3233/JIFS-201524
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9073-9084, 2021
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