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A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and Continuous Uncertain Evaluation.

Siyuan Wang1, Wenjun Ma1, Jieyu Zhan1

  • 1School of Computer Science, South China Normal University, Guangzhou 510631, China.

Entropy (Basel, Switzerland)
|November 11, 2022
PubMed
Summary

This study introduces a novel clustering method for multi-criteria decision-making (MCDM) using Dempster-Shafer (D-S) theory. The approach effectively handles both discrete and continuous uncertain evaluations, overcoming limitations of existing D-S theory applications.

Keywords:
D–S theorydecision making under uncertaintymulti-criteria decision makinguncertain information clustering

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Area of Science:

  • Decision Sciences
  • Artificial Intelligence
  • Information Science

Background:

  • Dempster-Shafer (D-S) theory is widely applied in multi-criteria decision-making (MCDM) for discrete uncertain evaluations.
  • Existing D-S theory methods struggle with continuous uncertain evaluations and expert-dependent mass function generation.
  • These limitations hinder real-world applications of D-S theory in MCDM, especially for large-scale problems.

Purpose of the Study:

  • To propose a novel clustering MCDM method that integrates D-S theory with the analytic hierarchy process (AHP) and the Silhouette coefficient.
  • To address the limitations of handling continuous uncertain evaluations and reduce the time and effort in mass function generation.
  • To provide a more robust and efficient approach for MCDM problems involving ambiguous decision alternatives.

Main Methods:

  • Utilized probability distributions and D-S theory to represent discrete and continuous ambiguous evaluations, respectively.
  • Employed clustering methods to determine focal element sets for mass functions.
  • Integrated the analytic hierarchy process (AHP) for assigning mass values and Dempster's combination rule for preference aggregation.

Main Results:

  • The proposed method successfully addresses the challenges of discrete and continuous uncertain evaluations in MCDM.
  • Demonstrated effectiveness in determining mass functions through clustering, reducing reliance on extensive expert judgment.
  • The integration of AHP and D-S theory proved rational, effective, and efficient in comparative analyses.

Conclusions:

  • The developed clustering MCDM method enhances D-S theory's applicability to complex decision-making scenarios.
  • Offers a significant improvement over existing methods by efficiently handling diverse uncertainty types.
  • Provides a practical and scalable solution for MCDM problems with ambiguous decision alternatives.