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Decision support system based on bipolar complex fuzzy Hamy mean operators.

Zhuoan Zhao1, Abrar Hussain2, Nan Zhang3

  • 1School of Economics and Management, Harbin Engineering University, Harbin, 150000, China.

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|September 12, 2024
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Summary
This summary is machine-generated.

This study introduces bipolar complex fuzzy set theory to multi-attribute decision-making (MADM) for better human opinion aggregation. New Hamy mean operators enhance decision algorithms for complex, uncertain real-world problems.

Keywords:
Aggregation operatorsBipolar complex fuzzy valuesHamy meanInvestment PolicyMulti-attribute decision-making

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Area of Science:

  • Decision Sciences
  • Fuzzy Mathematics
  • Computational Intelligence

Background:

  • Multi-attribute decision-making (MADM) requires effective aggregation of human opinions.
  • Existing fuzzy set theories may not fully capture the nuances of cognitive information.
  • Bipolar complex fuzzy sets offer a framework to represent both positive and negative aspects of opinions.

Purpose of the Study:

  • To extend multi-attribute decision-making (MADM) using bipolar complex fuzzy sets.
  • To develop novel aggregation operators for handling complex fuzzy information.
  • To provide a robust decision-making algorithm for real-life dilemmas.

Main Methods:

  • Exploration of bipolar complex fuzzy set concepts.
  • Derivation of bipolar complex fuzzy Hamy mean (BCFHM) and Dual Hamy mean (DHM) operators.
  • Development of bipolar complex fuzzy weighted Hamy mean (BCFWHM) and bipolar complex fuzzy weighted Dual Hamy mean (BCFWDHM) operators.
  • Establishment of a decision algorithm for MADM problems.

Main Results:

  • Novel BCFHM and DHM operators were derived, incorporating bipolar complex fuzzy information.
  • The proposed operators effectively handle uncertain information through additional parameters.
  • A case study demonstrated the compatibility and effectiveness of the developed approaches for investment policy evaluation.

Conclusions:

  • The developed bipolar complex fuzzy aggregation operators enhance MADM by providing a more nuanced way to aggregate cognitive information.
  • The proposed decision algorithm offers a compatible and consistent method for resolving real-life decision problems.
  • The research validates the advantages of the proposed methods through comparative analysis with existing operators.