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Granular Computing
Erschienen in: Granular Computing 2/2023

24.05.2022 | Original Paper

Multiple attribute group decision-making based on generalized interval-valued Pythagorean fuzzy Einstein geometric aggregation operators

verfasst von: Khaista Rahman

Erschienen in: Granular Computing | Ausgabe 2/2023

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Abstract

The aim of the paper is to develop the notion of some generalized interval-valued Pythagorean fuzzy aggregation operators using Einstein operational laws. Interval-valued Pythagorean fuzzy information are the good way to express the fuzzy information for decision and Einstein operations are the best approximations, and the generalized aggregation operators are a generalization of most aggregation operators. Thus, the main contribution of our this paper is to introduce three generalized aggregation operators based on interval-valued Pythagorean fuzzy numbers such as, the generalized interval-valued Pythagorean fuzzy Einstein-weighted geometric (GIVPFEWG) operator, the generalized interval-valued Pythagorean fuzzy Einstein ordered weighted geometric (GIVPFEOWG) operator, and the generalized interval-valued Pythagorean fuzzy Einstein hybrid geometric (GIVPFEHG) operator. Some of their desirable properties namely monotonicity, boundedness and idempotency are developed. Moreover, these methods are used for the selection of an expert and professional manager for a medicine company; for this, the specialists and experts of the problems deliver their favorites to display the practicality, and value of the new presented and developing valuable work.
Vorheriger Artikel Interval neutrosophic matrix game-based approach to counter cybersecurity issue
Nächster Artikel Extended MULTIMOORA method based on 2-tuple linguistic Pythagorean fuzzy sets for multi-attribute group decision-making

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Metadaten
Titel
Multiple attribute group decision-making based on generalized interval-valued Pythagorean fuzzy Einstein geometric aggregation operators
verfasst von
Khaista Rahman
Publikationsdatum
24.05.2022
Verlag
Springer International Publishing
Erschienen in
Granular Computing / Ausgabe 2/2023
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-022-00322-5

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