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The large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy

Yuting Zhu1, Wenyu Zhang1,2, Junjie Hou1

  • 1China Aerospace Academy of Systems Science and Engineering, Beijing 100048, China.

Mathematical Biosciences and Engineering : MBE
|March 29, 2024
PubMed
Summary

A new consensus method for large-scale group decisions uses probabilistic dual hesitant fuzzy sets to quantify expert opinions. This approach enhances decision-making in complex fuzzy environments with over 20 experts.

Keywords:
consensus-reaching modelgroup classification modellarge-scale groupmulti-attribute decision-makingprobabilistic dual hesitant fuzzy sets

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Area of Science:

  • Decision Sciences
  • Fuzzy Logic Systems
  • Operations Research

Background:

  • Large-scale group decision-making in fuzzy environments presents significant challenges.
  • Existing methods struggle to effectively integrate diverse expert opinions and uncertainties.
  • Quantifying expert judgments, including both membership and non-membership degrees, is crucial for robust decision outcomes.

Purpose of the Study:

  • To introduce a novel large-scale group consensus multi-attribute decision-making method.
  • To address the complexities of decision-making with numerous experts in fuzzy settings.
  • To effectively quantify and integrate expert decision information using probabilistic dual hesitant fuzzy sets.

Main Methods:

  • Constructing a probabilistic dual hesitant fuzzy information evaluation matrix and determining attribute weights.
  • Classifying expert groups and selecting optimal schemes based on effectiveness criteria.
  • Implementing consensus-reaching and information integration models for synchronized decision-making.

Main Results:

  • A validated method for large-scale group consensus multi-attribute decision-making.
  • Demonstrated effectiveness in a case study of urban emergency capability assessment.
  • Superiority shown through comparative analyses against alternative decision-making methods.

Conclusions:

  • The proposed method effectively handles large-scale group decision-making in fuzzy environments.
  • Probabilistic dual hesitant fuzzy sets provide a robust framework for quantifying expert uncertainty.
  • The method offers a superior approach for complex decision scenarios requiring consensus.