期刊
IEEE ACCESS
卷 11, 期 -, 页码 38548-38562出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3267512
关键词
Fuzzy sets; Decision making; Uncertainty; Correlation; Technological innovation; Sensitivity analysis; Linguistics; Fermatean hesitant fuzzy set; Choquet integral; Choquet integral aggregation operators; multi-attribute decision making
Fermatean fuzzy hesitant sets are a flexible and powerful decision-making tool that allows decision makers to incorporate uncertainty and hesitation into their decision-making process, resulting in more informed and effective decisions in complex and uncertain environments. The proposed Fermatean hesitant fuzzy Choquet integral ordered aggregation operators handle interdependent decision criteria or preferences and provide decisions under ideal and non-ideal situations. A multi-attribute decision-making method for Fermatean hesitant fuzzy information is presented using these operators, and its practicality and effectiveness are demonstrated through numerical examples.
Fermatean fuzzy hesitant sets provide a flexible and powerful tool for decision making, allowing decision makers to incorporate uncertainty and hesitation into their decision making process, and enabling them to make more informed and effective decisions in complex and uncertain environments. Considering that many factors are interdependent in reality while existing methods cannot solve this problem, we propose Fermatean hesitant fuzzy Choquet integral ordered aggregation operators based on the Fermatean hesitant fuzzy set and Choquet integral, and establish their related properties. These operators including averaging and geometric operators not only handle situations where decision criteria or preferences are interdependent, but also provides the decision under ideal and non-ideal situation. Additionally, we present a multi-attribute decision-making method for Fermatean hesitant fuzzy information using these operators. We have validated the proposed method, and its practicality and effectiveness are demonstrated through numerical examples from previous research regarding Fermatean hesitant fuzzy set, including sensitivity analysis and comparison.
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