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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Multi-attribute group decision making based on sine trigonometric spherical fuzzy aggregation operators.

Muhammad Qiyas1, Saleem Abdullah1, Saifullah Khan1

  • 1Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan.

Granular Computing
|April 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces novel sine trigonometric laws for spherical fuzzy sets (SFSs) to handle uncertainty in decision-making. New aggregation operators and a multi-attribute group decision-making (MAGDM) strategy are developed and applied to COVID-19 lab selection.

Keywords:
Decision makingSine trigonometric spherical fuzzy aggregation operatorsSpherical fuzzy sets

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Area of Science:

  • Fuzzy Set Theory
  • Decision Sciences
  • Computational Intelligence

Background:

  • Spherical fuzzy sets (SFSs) offer enhanced uncertainty handling compared to traditional fuzzy sets.
  • Existing fuzzy set structures have limitations in addressing complex decision problems.
  • Trigonometric functions, like sine, possess inherent properties of periodicity and symmetry beneficial for expert-driven multi-parameter analysis.

Purpose of the Study:

  • To introduce reliable sine trigonometric laws for spherical fuzzy sets.
  • To develop novel average and geometric aggregation operators for spherical fuzzy numbers.
  • To present a multi-attribute group decision-making (MAGDM) strategy utilizing these new operators.

Main Methods:

  • Development of sine trigonometric laws for SFSs.
  • Construction of new average and geometric aggregation operators for spherical fuzzy numbers.
  • Formulation of a MAGDM strategy based on the developed operators.

Main Results:

  • Successful definition of sine trigonometric laws for SFSs.
  • Introduction of effective average and geometric aggregation operators.
  • Demonstration of a viable MAGDM strategy with a practical application.

Conclusions:

  • The proposed sine trigonometric laws and aggregation operators enhance the capability of SFSs in decision-making.
  • The developed MAGDM strategy provides a robust framework for complex group decisions.
  • The application to COVID-19 laboratory selection validates the practical utility and effectiveness of the approach.