Tackling Complex Emergency Response Solutions Evaluation Problems in Sustainable Development by Fuzzy Group Decision...

Area of Science:

• Decision Sciences
• Operations Research
• Sustainable Development

Background:

• Economic development faces risks requiring robust emergency response solutions evaluation (ERSE).
• Traditional multiple criteria group decision making (MCGDM) struggles with decision hesitancy and criteria prioritization in ERSE.
• Complexity in practical ERSE problems necessitates advanced decision-making frameworks.

Purpose of the Study:

• To develop effective MCGDM approaches for ERSE problems characterized by decision hesitancy and prioritization.
• To introduce interval-valued dual hesitant fuzzy sets (IVDHFS) for comprehensive depiction of decision hesitancy.
• To address information distortion in existing models by defining a novel fuzzy entropy measure for IVDHFS.

Main Methods:

• Defined a fuzzy entropy measure for interval-valued dual hesitant fuzzy sets (IVDHFS).
• Developed two prioritized operators for IVDHFS based on the defined entropy measure.
• Constructed two hesitant fuzzy MCGDM approaches for scenarios with and without known decision maker weights.

Main Results:

• The proposed fuzzy entropy measure for IVDHFS avoids information distortion common in distance-based measures.
• The developed prioritized operators effectively handle prioritization relations among criteria.
• Case studies demonstrated the effectiveness and practicality of the proposed MCGDM approaches for ERSE.

Conclusions:

• The novel IVDHFS-based MCGDM approaches provide a robust framework for complex ERSE.
• The methods enhance the accuracy and reliability of ERSE by managing decision hesitancy and criteria prioritization.
• This research contributes to improved governance of sustainable development through better risk preparedness.