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Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to

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Summary
This summary is machine-generated.

This study introduces Complex Polytopic Fuzzy Sets (CPoFS) to better represent uncertainty. New operators and an algorithm are developed for improved multiattribute decision-making in complex, uncertain environments.

Keywords:
Aggregation operatorsCPoFSsConfidence levelDecision-making process

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Area of Science:

  • Fuzzy Set Theory
  • Decision Making
  • Uncertainty Quantification

Background:

  • Traditional fuzzy sets struggle with complex uncertainty.
  • Polytopic Fuzzy Sets (PoFS) offer an improvement.
  • Complex Polytopic Fuzzy Sets (CPoFS) extend PoFS for nuanced uncertainty representation.

Purpose of the Study:

  • Develop Complex Polytopic Fuzzy Sets (CPoFS) and their operational laws.
  • Introduce novel confidence-level-based aggregation operators for CPoFS.
  • Create an algorithm for multiattribute decision-making using CPoFS.

Main Methods:

  • Defined basic operational laws for CPoFS.
  • Introduced five new aggregation operators: CCPoFEWGA, CCPoFEOWGA, CCPoFEHGA, I-CCPoFEOWGA, I-CCPoFEHGA.
  • Investigated operator properties (monotonicity, boundedness, idempotency).
  • Developed and applied a CPoFS-based algorithm to a multiattribute decision-making problem.

Main Results:

  • Established foundational operational laws for CPoFS.
  • Demonstrated the utility of new aggregation operators in enhancing decision precision.
  • Validated the proposed algorithm's effectiveness through a numerical example.
  • Showcased the method's flexibility and superiority over existing approaches.

Conclusions:

  • CPoFS provide a more sophisticated framework for handling uncertainty.
  • The developed operators and algorithm significantly improve multiattribute decision-making.
  • The proposed method offers enhanced performance and adaptability in complex scenarios.