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A decision-making technique under interval-valued Fermatean fuzzy Hamacher interactive aggregation operators.

Gulfam Shahzadi1, Anam Luqman2, Faruk Karaaslan3

  • 1Department of Mathematics, Garrison Post Graduate College, Lahore Cantt., Pakistan.

Soft Computing
|June 26, 2023
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Summary

This study introduces new aggregation operators for interval-valued Fermatean fuzzy numbers to solve multi-attribute decision-making (MADM) problems. The developed technique enhances accuracy and provides a more complete approach for decision-makers in real-world scenarios.

Keywords:
AOsHamacher interactive operatorsIVFF numbersMADMMine emergency decision-making

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Area of Science:

  • Decision Sciences
  • Fuzzy Logic Systems
  • Operations Research

Background:

  • Multi-attribute decision-making (MADM) challenges exist with complex, uncertain data.
  • Interval-valued Fermatean fuzzy numbers offer a robust framework for uncertainty.
  • Existing MADM methods may lack comprehensive handling of such data.

Purpose of the Study:

  • To develop novel aggregation operators for interval-valued Fermatean fuzzy numbers.
  • To establish a new MADM technique utilizing these operators.
  • To demonstrate the method's efficacy through a practical case study.

Main Methods:

  • Introduction of Hamacher interactive aggregation operators (weighted, ordered weighted, hybrid weighted).
  • Investigation of the distinct characteristics of the proposed operators.
  • Application of the operators to construct an MADM technique for interval-valued Fermatean fuzzy information.

Main Results:

  • A novel MADM technique was successfully developed and applied.
  • A case study on mine emergency plan selection validated the method's practicality.
  • Analysis of parametric influence showed the method's adaptability.

Conclusions:

  • The proposed aggregation operators provide a progressively complete decision-making approach.
  • The developed MADM technique offers enhanced accuracy and practical outcomes.
  • This method is valuable for real-life MADM problems involving uncertainty.