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Large-scale group-hierarchical DEMATEL method for complex systems.

Wenyu Chen1,2, Weimin Li2, Lei Shao2

  • 1Graduate Collage, Air Force Engineering University, Xi'an, China.

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This summary is machine-generated.

This study introduces a hierarchical Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to improve factor identification in complex systems. The new approach effectively integrates large-scale group decision-making and expert knowledge for more reliable results.

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Area of Science:

  • Systems Engineering
  • Decision Science
  • Operations Research

Background:

  • Traditional Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are limited to simple systems.
  • Existing methods struggle to integrate knowledge from large-scale expert groups, impacting initial direct relation (IDR) matrix quality.
  • Complex systems require robust methods for accurate critical factor identification.

Purpose of the Study:

  • To propose a hierarchical DEMATEL method for large-scale group decision-making in complex systems.
  • To enhance the integration of expert knowledge and experience in DEMATEL.
  • To improve the accuracy and reliability of critical factor identification.

Main Methods:

  • Development of a hierarchical DEMATEL approach for complex systems.
  • Introduction of an expert consistency network to construct expert weight matrices.
  • Utilizing the clustering coefficient to determine expert weights for different elements.
  • Summarizing steps for identifying key elements using the large-scale group-hierarchical DEMATEL method.

Main Results:

  • The proposed method effectively handles large-scale group decision-making in complex systems.
  • The expert consistency network enhances the integration of diverse expert knowledge.
  • Robustness analysis in an interference environment demonstrated algorithm stability.
  • A case study verified the method's superiority over existing approaches.

Conclusions:

  • The hierarchical DEMATEL method is suitable for group decision-making in complex systems.
  • The proposed approach offers high algorithm stability and low deviation.
  • This method provides a more reliable tool for identifying critical factors in intricate environments.