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Granular Computing

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05.08.2022 | Original Paper

Extended CODAS method for multi-attribute group decision-making based on 2-tuple linguistic Fermatean fuzzy Hamacher aggregation operators

verfasst von: Muhammad Akram, Zohra Niaz, Feng Feng

Erschienen in: Granular Computing | Ausgabe 3/2023

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Abstract

The 2-tuple linguistic model initiated by Herrera and Martinez has become one of the most useful computing with words approaches. It provides an effective way of representing the linguistic evaluation in terms of 2-tuples to avoid information loss in decision-making process. This research aims at extending the combinative distance based assessment (CODAS) method by virtue of 2-tuple linguistic Fermatean fuzzy Hamacher aggregation operators. We put forth several different types of 2-tuple linguistic Fermatean fuzzy aggregation operators, including the 2-tuple linguistic Fermatean fuzzy Hamacher weighted averaging operator, the 2-tuple linguistic Fermatean fuzzy Hamacher weighted geometric operator, the 2-tuple linguistic Fermatean fuzzy Hamacher Hybrid averaging operator, and the 2-tuple linguistic Fermatean fuzzy Hamacher Hybrid geometric operator. Some fundamental properties of the proposed 2-tuple linguistic Fermatean fuzzy aggregation operators are investigated. Furthermore, we develop a 2-tuple linguistic Fermatean fuzzy CODAS method with the proposed aggregation operators for solving multi-attribute group decision-making problems. Numerical examples regarding the selection of construction companies and McDonald’s franchisees are used to show the effectiveness of the proposed extended CODAS approach. Finally, a comparative analysis is conducted to validate the proposed approach and show its advantages.

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Titel: Extended CODAS method for multi-attribute group decision-making based on 2-tuple linguistic Fermatean fuzzy Hamacher aggregation operators
verfasst von: Muhammad Akram
Zohra Niaz
Feng Feng
Publikationsdatum: 05.08.2022
Verlag: Springer International Publishing
Erschienen in: Granular Computing / Ausgabe 3/2023
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-022-00332-3

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