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Generalized Grey Target Decision Method for Mixed Attributes Based on Kullback-Leibler Distance.

Jinshan Ma1

  • 1School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

A new generalized grey target decision method uses Kullback-Leibler (K-L) distance for mixed attributes. This approach enhances decision-making by integrating K-L distances with TOPSIS for improved accuracy.

Keywords:
Kullback-Leibler distanceTOPSISbinary connection numbergeneralized grey target decision methodmixed attributes

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Area of Science:

  • Decision Sciences
  • Operations Research
  • Data Analysis

Background:

  • Multi-attribute decision-making (MADM) often involves complex data with mixed attributes.
  • Existing methods may struggle with uncertainty and determinacy in attribute values.
  • A robust method is needed to handle heterogeneous data and provide clear decision outcomes.

Purpose of the Study:

  • To propose a novel generalized grey target decision method for mixed attributes.
  • To incorporate Kullback-Leibler (K-L) distance for measuring attribute differences.
  • To enhance decision-making accuracy in complex scenarios.

Main Methods:

  • Conversion of indices into index binary connection number vectors.
  • Derivation of two-tuple (determinacy, uncertainty) numbers from vectors.
  • Calculation of positive and negative target centers.
  • Integration of K-L distances using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS).

Main Results:

  • The proposed method effectively handles mixed attributes by converting them into a unified format.
  • Kullback-Leibler (K-L) distance quantifies the divergence between alternatives and target centers.
  • The integration with TOPSIS provides a robust mechanism for ranking alternatives based on integrated values.

Conclusions:

  • The generalized grey target decision method offers a powerful tool for complex decision problems.
  • The approach provides a systematic way to handle mixed attributes and uncertainty.
  • The case study demonstrates the practical applicability and effectiveness of the proposed method.