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A Novel Multi-Attribute Decision-Making Method Based on Linguistic Fermatean Fuzzy Sets and Power Average Operator

In: Ieis 2022

Author

Listed:
  • Xue Feng

    (Beijing Jiaotong University)

  • Jun Wang

    (Beijing Jiaotong University)

  • Yuping Xing

    (Donghua University)

Abstract

This paper studies a novel tool for describing fuzzy information, called linguistic Fermatean fuzzy sets (LFFSs), in the process of multi-attribute decision-making (MADM). Compared to linguistic intuitionistic fuzzy sets and linguistic Pythagorean fuzzy sets, our LFFSs are more flexible and can depict more complicated decision-making information then the former two. In this study, we first introduce the notion of LFFSs. Afterwards, some other related concepts, such as operational rules, ranking methods as well as distance measure are interpreted. When considering aggregation operators for linguistic Fermatean fuzzy information, we generalize the classical power average (PA) operator into LFFSs and introduce the linguistic Fermatean fuzzy power average operator and its weighted form. Subsequently, a new MADM method based on LFFSs and their aggregation operator is developed. At last, an illustrative example is provided to show how our proposed method can be applied in solving realistic MADM problems.

Suggested Citation

  • Xue Feng & Jun Wang & Yuping Xing, 2023. "A Novel Multi-Attribute Decision-Making Method Based on Linguistic Fermatean Fuzzy Sets and Power Average Operator," Lecture Notes in Operations Research, in: Menggang Li & Guowei Hua & Xiaowen Fu & Anqiang Huang & Dan Chang (ed.), Ieis 2022, pages 30-42, Springer.
    • Handle: RePEc:spr:lnopch:978-981-99-3618-2_4
    DOI: 10.1007/978-981-99-3618-2_4
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