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Hesitant bi-fuzzy multi-attribute information fusion with expert reliability
Shshank Chaube1, Anuj Kumar2, Manoj Kumar Singh3
1Symbiosis Institute of Technology, Hyderabad Campus, Symbiosis International (Deemed University), Pune, India. shshank.chaube@sithyd.siu.edu.in.
Scientific Reports
|April 24, 2025
Summary
This study introduces a hesitant bi-fuzzy information fusion (HBFIF) method to improve multi-attribute decision-making (MADM) by integrating expert reliability (ER). The approach effectively handles conflicting expert opinions for more precise outcomes.
Area of Science:
- Decision Sciences
- Artificial Intelligence
- Operations Research
Background:
- Multi-attribute decision-making (MADM) requires expert input, but expert reliability and conflicting opinions can reduce outcome precision.
- Existing methods struggle to fully integrate expert hesitancy and conflicts while preserving original data integrity.
- Expert reliability (ER) is crucial for accurate decision outcomes in complex scenarios.
Purpose of the Study:
- To propose a novel hesitant bi-fuzzy information fusion (HBFIF) method for MADM.
- To integrate expert reliability (ER) into decision-making frameworks, especially when dealing with conflicting expert information.
- To enhance the aggregation of expert preferences while preserving original data and risk preferences.
Main Methods:
- Developed a hesitant bi-fuzzy information fusion (HBFIF) method incorporating expert reliability (ER).
- Constructed a reliability metric based on the similarity of expert opinions.
- Utilized the power average (PA) operator and TOPSIS-inspired approach for data aggregation and opinion preservation.
Main Results:
- The HBFIF method effectively integrates expert reliability into decision-making processes.
- The approach successfully handles hesitant and conflicting expert information.
- Demonstrated applicability and effectiveness through selection of hydrogen storage methods for automobiles.
Conclusions:
- The proposed HBFIF method offers a robust framework for MADM with unreliable and conflicting expert data.
- Integrating expert reliability enhances the accuracy and integrity of decision outcomes.
- The method has practical relevance and applicability in real-world decision-making scenarios, such as automotive technology selection.
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