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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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A New Multi-Attribute Emergency Decision-Making Algorithm Based on Intuitionistic Fuzzy Cross-Entropy and

Ping Li¹, Ying Ji¹, Zhong Wu¹

¹Business School, University of Shanghai for Science and Technology, Shanghai 200000, China.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This study introduces a new model for multi-attribute emergency decision-making (MAEDM) using intuitionistic fuzzy numbers and grey correlation analysis. The improved method enhances accuracy by addressing information loss in traditional approaches.

Keywords:

attribute weights earthquake shelters grey correlation analysis intuitionistic fuzzy cross-entropy multi-attribute emergency decision-making

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Area of Science:

Decision Sciences
Artificial Intelligence
Operations Research

Background:

Traditional intuitionistic fuzzy distance measurements struggle with membership and non-membership data, leading to information confusion in complex decision-making.
Multi-attribute emergency decision-making (MAEDM) requires robust methods to handle uncertainty and incomplete information.

Purpose of the Study:

To develop an improved decision-making model for MAEDM problems with unknown or partially known attribute weights.
To overcome the limitations of traditional intuitionistic fuzzy distance methods by reducing information loss and enhancing accuracy.

Main Methods:

Constructed a decision-making model utilizing intuitionistic fuzzy cross-entropy and a comprehensive grey correlation analysis algorithm.
Developed a grey correlation analysis algorithm integrating objective evaluation and subjective preference values for decision-makers.
Applied the model to a case study of shelter ranking following the Wenchuan Earthquake.

Main Results:

The proposed model significantly improves the accuracy of MAEDM by compensating for information loss inherent in traditional methods.
Sensitivity analysis confirmed the stability of shelter ranking results as the grey resolution coefficient varied.
The new method demonstrated superior reliability compared to traditional intuitionistic fuzzy distance measurements.

Conclusions:

The developed intuitionistic fuzzy cross-entropy and grey correlation analysis model offers a more reliable and accurate approach to MAEDM.
This method effectively handles attribute weight uncertainty and decision-maker preferences, providing stable and robust results.
The findings have practical implications for emergency management and resource allocation in real-world crisis scenarios.