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Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators

A useful expansion of the intuitionistic fuzzy set (IFS) for dealing with ambiguities in information is the Pythagorean fuzzy set (PFS), which is one of the most frequently used fuzzy sets in data science. Due to these circumstances, the Aczel-Alsina operations are used in this study to formulate se...

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Autores principales: Senapati, Tapan, Chen, Guiyun, Mesiar, Radko, Saha, Abhijit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366135/
https://www.ncbi.nlm.nih.gov/pubmed/35971560
http://dx.doi.org/10.1007/s12652-022-04360-4
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author Senapati, Tapan
Chen, Guiyun
Mesiar, Radko
Saha, Abhijit
author_facet Senapati, Tapan
Chen, Guiyun
Mesiar, Radko
Saha, Abhijit
author_sort Senapati, Tapan
collection PubMed
description A useful expansion of the intuitionistic fuzzy set (IFS) for dealing with ambiguities in information is the Pythagorean fuzzy set (PFS), which is one of the most frequently used fuzzy sets in data science. Due to these circumstances, the Aczel-Alsina operations are used in this study to formulate several Pythagorean fuzzy (PF) Aczel-Alsina aggregation operators, which include the PF Aczel-Alsina weighted average (PFAAWA) operator, PF Aczel-Alsina order weighted average (PFAAOWA) operator, and PF Aczel-Alsina hybrid average (PFAAHA) operator. The distinguishing characteristics of these potential operators are studied in detail. The primary advantage of using an advanced operator is that it provides decision-makers with a more comprehensive understanding of the situation. If we compare the results of this study to those of prior strategies, we can see that the approach proposed in this study is more thorough, more precise, and more concrete. As a result, this technique makes a significant contribution to the solution of real-world problems. Eventually, the suggested operator is put into practise in order to overcome the issues related to multi-attribute decision-making under the PF data environment. A numerical example has been used to show that the suggested method is valid, useful, and effective.
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spelling pubmed-93661352022-08-11 Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators Senapati, Tapan Chen, Guiyun Mesiar, Radko Saha, Abhijit J Ambient Intell Humaniz Comput Original Research A useful expansion of the intuitionistic fuzzy set (IFS) for dealing with ambiguities in information is the Pythagorean fuzzy set (PFS), which is one of the most frequently used fuzzy sets in data science. Due to these circumstances, the Aczel-Alsina operations are used in this study to formulate several Pythagorean fuzzy (PF) Aczel-Alsina aggregation operators, which include the PF Aczel-Alsina weighted average (PFAAWA) operator, PF Aczel-Alsina order weighted average (PFAAOWA) operator, and PF Aczel-Alsina hybrid average (PFAAHA) operator. The distinguishing characteristics of these potential operators are studied in detail. The primary advantage of using an advanced operator is that it provides decision-makers with a more comprehensive understanding of the situation. If we compare the results of this study to those of prior strategies, we can see that the approach proposed in this study is more thorough, more precise, and more concrete. As a result, this technique makes a significant contribution to the solution of real-world problems. Eventually, the suggested operator is put into practise in order to overcome the issues related to multi-attribute decision-making under the PF data environment. A numerical example has been used to show that the suggested method is valid, useful, and effective. Springer Berlin Heidelberg 2022-08-11 /pmc/articles/PMC9366135/ /pubmed/35971560 http://dx.doi.org/10.1007/s12652-022-04360-4 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Senapati, Tapan
Chen, Guiyun
Mesiar, Radko
Saha, Abhijit
Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators
title Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators
title_full Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators
title_fullStr Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators
title_full_unstemmed Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators
title_short Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators
title_sort multiple attribute decision making based on pythagorean fuzzy aczel-alsina average aggregation operators
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366135/
https://www.ncbi.nlm.nih.gov/pubmed/35971560
http://dx.doi.org/10.1007/s12652-022-04360-4
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