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.
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Acknowledgment
This work was supported by Shanghai Science and Technology Development Funds (Yang Fan Program, Grant no. 22YF1401400).
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Feng, X., Wang, J., Xing, Y. (2023). A Novel Multi-Attribute Decision-Making Method Based on Linguistic Fermatean Fuzzy Sets and Power Average Operator. In: Li, M., Hua, G., Fu, X., Huang, A., Chang, D. (eds) IEIS 2022. ICIEIS 2022. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-99-3618-2_4
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