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RETRACTED ARTICLE: Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation

Tapan Senapati1, Guiyun Chen1, Radko Mesiar2,3

  • 1School of Mathematics and Statistics, Southwest University, Beibei, 400715 Chongqing China.

Journal of Ambient Intelligence and Humanized Computing
|August 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces new Pythagorean fuzzy (PF) Aczel-Alsina aggregation operators to handle complex data ambiguities. These operators offer a more precise and effective approach for multi-attribute decision-making problems.

Keywords:
Aczel-Alsina operationsMADMPythagorean fuzzy Aczel-Alsina average aggregation operatorsPythagorean fuzzy elements

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Area of Science:

  • Data Science
  • Fuzzy Set Theory
  • Decision Support Systems

Background:

  • Intuitionistic fuzzy sets (IFS) are useful for ambiguity but have limitations.
  • Pythagorean fuzzy sets (PFS) extend IFS, offering greater flexibility in representing uncertainty.
  • PFS are increasingly vital in data science for handling complex decision-making scenarios.

Purpose of the Study:

  • To formulate novel Pythagorean fuzzy (PF) Aczel-Alsin aggregation operators.
  • To investigate the properties of these new operators, including PF Aczel-Alsina weighted average (PFAAWA), PF Aczel-Alsina order weighted average (PFAAOWA), and PF Aczel-Alsina hybrid average (PFAAHA).
  • To apply these operators to multi-attribute decision-making (MADM) problems within a PF data environment.

Main Methods:

  • Development of PF Aczel-Alsina aggregation operators (PFAAWA, PFAAOWA, PFAAHA).
  • Detailed analysis of the characteristics and properties of the proposed operators.
  • Application of the developed operators to solve MADM problems using PF data.

Main Results:

  • The proposed PF Aczel-Alsina aggregation operators provide a more comprehensive understanding for decision-makers.
  • The new operators demonstrate superior thoroughness, precision, and concreteness compared to existing methods.
  • A numerical example validates the effectiveness and practical applicability of the proposed approach.

Conclusions:

  • The developed PF Aczel-Alsina aggregation operators represent a significant advancement in handling data ambiguity.
  • This methodology offers a robust framework for addressing real-world MADM challenges in a PF data context.
  • The study confirms the validity, utility, and effectiveness of the proposed operators for practical applications.