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Decision making under measure-based granular uncertainty with intuitionistic fuzzy sets
1Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, 610054 China.
Applied Intelligence (Dordrecht, Netherlands)
|November 12, 2021
Summary
This study introduces a new decision-making model for intuitionistic fuzzy environments, extending measure-based granular uncertainty methods. The proposed approach effectively handles complex decisions where traditional models fall short.
Area of Science:
- Decision Sciences
- Fuzzy Set Theory
- Artificial Intelligence
Background:
- Traditional decision-making under measure-based granular uncertainty struggles in complex, real-world intuitionistic fuzzy environments.
- Existing models are not designed for the nuances of intuitionistic fuzzy sets, limiting their applicability.
- There is an open research gap in adapting granular uncertainty decision-making to intuitionistic fuzzy settings.
Purpose of the Study:
- To propose a novel decision-making framework for measure-based granular uncertainty within intuitionistic fuzzy sets.
- To extend the capabilities of measure-based granular uncertainty to effectively address decision problems in intuitionistic fuzzy environments.
- To validate the proposed model's efficacy through numerical examples and comparative analysis.
Main Methods:
- Development of a new decision-making model integrating intuitionistic fuzzy sets with measure-based granular uncertainty.
- Application of the Choquet integral, measures, and representative payoffs within the new framework.
- Validation using numerical examples and comparison with existing decision-making under measure-based granular uncertainty models.
Main Results:
- The proposed model successfully represents objects and facilitates effective decision-making in intuitionistic fuzzy environments.
- Numerical examples confirm the validity and effectiveness of the developed decision-making approach.
- Comparative analysis demonstrates the proposed model's superior performance, solving problems intractable for previous methods.
Conclusions:
- The developed decision-making under measure-based granular uncertainty with intuitionistic fuzzy sets is a valid extension of existing theories.
- This new model significantly enhances decision-making capabilities in complex intuitionistic fuzzy environments.
- The findings offer a more robust tool for applied intelligence and uncertain decision analysis.
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