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A Decision-Making Method with Grey Multi-Source Heterogeneous Data and Its Application in Green Supplier Selection.

Huifang Sun1, Yaoguo Dang2, Wenxin Mao3

  • 1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. sunhuifang2014@163.com.

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This study introduces a novel decision-making method for complex problems involving grey multi-source heterogeneous data. The approach effectively processes diverse data types to improve multi-attribute decision-making and green supplier selection.

Keywords:
green supplier selectiongrey multi-source heterogeneous datakernel and greyness degreemulti-attribute decision making

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Area of Science:

  • Operations Research
  • Decision Science
  • Data Science

Background:

  • Multi-attribute decision-making (MADM) problems often involve attribute values that are grey, multi-source, and heterogeneous.
  • Existing methods struggle to effectively integrate and analyze such complex data structures.
  • The need for robust methods to handle uncertainty and data diversity in decision-making is critical.

Purpose of the Study:

  • To propose a novel MADM method capable of handling grey multi-source heterogeneous data.
  • To develop techniques for quantifying and integrating the 'kernel' and 'greyness degree' of data.
  • To establish a framework for determining hierarchical attribute weights considering attribute interdependencies.

Main Methods:

  • Defined kernel and greyness degree for extended grey numbers within heterogeneous data sequences.
  • Constructed kernel and greyness degree vectors to 'whiten' multi-source heterogeneous information.
  • Developed a grey relational bi-directional projection ranking method and the HG-DEMATEL method for hierarchical attribute weighting.

Main Results:

  • Successfully demonstrated a method to process and integrate grey multi-source heterogeneous data.
  • The proposed grey relational bi-directional projection ranking effectively ranks alternatives.
  • The HG-DEMATEL method accurately determines hierarchical attribute weights, accounting for causalities.

Conclusions:

  • The proposed kernel and greyness degree-based decision-making method is rational and valid for MADM problems with complex data.
  • The approach provides a robust solution for green supplier selection and similar complex decision scenarios.
  • This method enhances the ability to handle uncertainty and heterogeneity in decision-making data.