Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Area of Science:

• Decision Sciences
• Artificial Intelligence
• Information Science

Background:

• Emergency situations are characterized by time constraints, limited information, and inherent uncertainty.
• Effective decision-making in emergencies is critical but challenged by ambiguity and risk.
• Existing fuzzy structures may not fully capture the complexity of emergency decision-making problems.

Purpose of the Study:

• To introduce novel approaches for emergency decision-making using spherical fuzzy sets (FS).
• To address uncertainty and ambiguity in decision-making problems (DMPs) within emergency contexts.
• To develop and apply methodologies for the COVID-19 emergency situation.

Main Methods:

• Developed novel algebraic operational laws (AOLs) for spherical fuzzy sets.
• Introduced Einstein aggregation operators (AgOp) for uncertain information.
• Extended TOPSIS and Gray relational analysis with unknown weight criteria.
• Designed three algorithms for emergency DMPs under spherical fuzzy environments.

Main Results:

• The proposed novel AOLs under spherical fuzzy settings were validated.
• Einstein aggregation operators effectively aggregated uncertain information.
• Extended decision-making approaches and algorithms demonstrated effectiveness in handling uncertainty.
• The methodologies provided accurate emergency measures for the COVID-19 situation.

Conclusions:

• Spherical fuzzy sets offer a generalized framework for handling uncertainty in emergency DMPs.
• The proposed novel methodologies and algorithms are effective for emergency decision-making.
• Accurate emergency measures can be derived to address global uncertainties like COVID-19.