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Multi-attribute decision-making method based on complex T-spherical fuzzy frank prioritized aggregation operators.

Muhammad Rizwan Khan1, Kifayat Ullah1, Ali Raza1

  • 1Department of Mathematics, Riphah International University Lahore, Lahore, 54000, Pakistan.

Heliyon
|February 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces novel complex T-spherical fuzzy aggregation operators (AOs) with priority degrees for enhanced uncertain data handling. These new operators are applied to solve multi-attribute decision-making problems, demonstrating superior performance.

Keywords:
Aggregation operatorsComplex T-spherical fuzzy setDecision-makingFrank t-norm and t-conormMulti-attribute decision-making

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Area of Science:

  • Fuzzy Set Theory
  • Decision Sciences
  • Information Fusion

Background:

  • Existing fuzzy set theories (IFS, PyFS, q-ROFS, PFS, SFS) have limitations in handling complex uncertain data.
  • The complex T-spherical fuzzy (TSF) set offers a more generalized framework for data aggregation.
  • Priority degrees are introduced to refine aggregation processes.

Purpose of the Study:

  • To develop new complex T-spherical fuzzy frank prioritized (CTSFFP) aggregation operators (AOs).
  • To explore the properties of these new AOs, including idempotency, monotonicity, and boundedness.
  • To demonstrate the practical application of the developed AOs in multi-attribute decision-making (MADM) problems.

Main Methods:

  • Development of CTSFFP aggregation operators using frank t-norm and frank t-conorm operational laws.
  • Explanation of sum, product, and power operations within the complex TSF information context.
  • Application of CTSFFP weighted averaging (CTSFFPWA) and CTSFFP weighted geometric (CTSFFPWG) operators to solve a real-life MADM problem.

Main Results:

  • Introduction of six new CTSFFP AOs: CTSFFPWA, CTSFFPOWA, CTSFFPHWA, CTSFFPWG, CTSFFPOWG, and CTSFFPHWG.
  • Analysis of the fundamental properties of the proposed AOs.
  • Successful application of CTSFFPWA and CTSFFPWG operators in a MADM scenario, validating their effectiveness.

Conclusions:

  • The proposed CTSFFP AOs provide a powerful and flexible tool for handling complex uncertain information.
  • The TSF set framework, with the integration of priority degrees, offers advantages over existing fuzzy set structures.
  • The study highlights the superiority of the developed AOs through comparison with existing methods in MADM problems.