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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Integrated Decision-Making Method for Heterogeneous Attributes Based on Probabilistic Linguistic Cross-Entropy and

Lei Wang1, Huifeng Xue1

  • 1China Academy of Aerospace Systems Science and Engineering, Beijing 100035, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a novel meta-synthesis decision-making method using probabilistic linguistic cross-entropy. It effectively handles complex problems with both qualitative and quantitative data for better decision outcomes.

Keywords:
cross-entropyentropyheterogeneous attribute informationmeta-synthesispriority relationprobabilistic linguistic term set

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Area of Science:

  • Decision Sciences
  • Operations Research
  • Information Science

Background:

  • Meta-synthesis has proven effective in complex fields like aerospace engineering.
  • Existing methods struggle with integrating qualitative and quantitative data in decision-making.
  • Entropy theory offers robust tools for quantitative evaluation and fuzzy decision-making.

Purpose of the Study:

  • To propose a novel meta-synthesis decision-making method for multicriteria problems.
  • To integrate qualitative and quantitative data using probabilistic linguistic cross-entropy and priority relations.
  • To enhance decision-making by comprehensively considering multivariate heterogeneous attributes.

Main Methods:

  • Utilizes the entropy weight method for quantitative attribute weighting.
  • Employs probabilistic linguistic entropy and cross-entropy for qualitative attribute weighting, considering interactions.
  • Integrates weights using a weight preference coefficient and applies a 0-1 priority relation matrix for final ranking.

Main Results:

  • Successfully calculates attribute weights by combining entropy-based and probabilistic linguistic approaches.
  • Provides a standardized method for integrating heterogeneous attribute weights.
  • Achieves a comprehensive ranking of alternatives by analyzing advantages and disadvantages across all criteria.

Conclusions:

  • The proposed meta-synthesis method effectively addresses multicriteria decision-making with mixed data types.
  • The method demonstrates superiority and effectiveness through numerical example validation.
  • Offers a valuable framework for complex decision problems in various engineering and economic domains.