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Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale.

Qingsheng Li1, Yuzhu Diao2, Zaiwu Gong3

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Area of Science:

  • Decision Sciences
  • Operations Research
  • Environmental Science

Background:

  • Traditional grey language decision-making methods can suffer from information distortion.
  • Hesitant fuzzy group decision-making requires robust methods to handle uncertainty.
  • Evaluating sustainable development requires accurate multi-attribute analysis.

Purpose of the Study:

  • To propose a novel kernel and grey scale scoring function for grey language multi-attribute group decision-making.
  • To integrate grey scale into decision-making to mitigate information distortion.
  • To apply and validate the proposed method in a circular economy context.

Main Methods:

  • Development of a kernel and grey scale scoring function based on grey language principles.
  • Application to grey language hesitant fuzzy group decision-making scenarios.
  • Utilizing grey correlation degree for scheme ranking and validation through comparative analysis.

Main Results:

  • The proposed function effectively reduces information distortion in decision-making.
  • The method demonstrates superior effectiveness and practicality in evaluating industry chain sustainability.
  • Comparative analysis confirms the simplicity and feasibility against traditional and GLHWAA methods.

Conclusions:

  • The kernel and grey scale scoring function offers an improved approach to grey language decision-making.
  • The method is particularly effective for hesitant fuzzy group decision-making and sustainability assessments.
  • This approach provides a reliable and feasible tool for complex decision problems.