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Multiattribute decision-making under Fermatean fuzzy bipolar soft framework.

Ghous Ali1, Masfa Nasrullah Ansari1

  • 1Division of Science and Technology, Department of Mathematics, University of Education, Lahore, Pakistan.

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

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Corrigendum to "Selecting optimal celestial object for space observation in the realm of complex spherical fuzzy systems" [Heliyon Volume 10, Issue 13, July 2024, Article e32897].

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Advanced bioactuator selection for efficient human motion simulation in biohybrid robots using CSF dynamic knowledge.

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A New Group Decision-Making Technique under Picture Fuzzy Soft Expert Information.

Entropy (Basel, Switzerland)·2021

Fermatean fuzzy set theory offers a new way to manage uncertainty. This study introduces the Fermatean fuzzy bipolar soft set model, enhancing decision-making in complex scenarios.

Area of Science:

  • Mathematics
  • Computer Science
  • Decision Science

Background:

  • Fermatean fuzzy sets provide a robust framework for handling uncertainty, outperforming intuitionistic and Pythagorean fuzzy sets.
  • Existing fuzzy set models have limitations in capturing the nuances of real-world decision-making under uncertainty.

Purpose of the Study:

  • To introduce and investigate the novel Fermatean fuzzy bipolar soft set (FFBSS) model.
  • To extend existing fuzzy bipolar soft set and Pythagorean fuzzy bipolar soft set models.
  • To explore the applicability of the FFBSS model in multi-attribute decision-making (MADM).

Main Methods:

  • Developed fundamental properties of the FFBSS model, including subset-hood, equality, and operations (intersection, union, AND, OR).
  • Proposed basic operations such as Fermatean fuzzy weighted average and score function for FFBSS.
Keywords:
AlgorithmDecision-makingFermatean fuzzy bipolar soft setFermatean fuzzy setScore function

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  • Utilized a novel algorithm to support the proposed FFBSS methodology for MADM problems.

    • Main Results:

    • Investigated and defined key properties and operations for the FFBSS model with numerical examples.
    • Demonstrated the effectiveness of the FFBSS model through two distinct MADM applications.
    • Provided a comparative analysis highlighting the advantages of FFBSS over existing models.

    Conclusions:

    • The FFBSS model offers a more flexible and reliable approach to handling uncertainty in decision-making.
    • The proposed model and its associated operations are effective for various multi-attribute decision-making problems.
    • FFBSS represents a significant advancement in fuzzy set theory for complex real-world applications.