Multi-objective evolutionary emergency response optimization for major accidents.

Area of Science:

• Operations Research
• Risk Management
• Public Health

Background:

• Effective emergency response planning is crucial for protecting public and worker health during major accidents like hazardous material events or nuclear accidents.
• Protective actions such as evacuation, in-building sheltering, and personal protective equipment are key components of emergency response.
• Optimizing emergency response is complex due to multiple, often conflicting, criteria including health consequences, social disruption, and economic costs.

Purpose of the Study:

• To present a novel methodology for multi-objective optimization of emergency response planning for major accidents.
• To optimize the emergency policy regarding the selection and implementation of protective actions.
• To demonstrate the application of this methodology through case studies.

Main Methods:

• Development of a multi-objective optimization framework for emergency response.
• Utilization of an evolutionary algorithm as the core optimization tool.
• Application and validation through case studies involving hazardous material incidents.

Main Results:

• The methodology provides a systematic approach to optimizing emergency response policies.
• Demonstrated effectiveness in decision-making for hazardous material accidents.
• The optimized plans consider multiple criteria to achieve a balanced response.

Conclusions:

• The proposed methodology offers a robust tool for multi-objective optimization in emergency response planning.
• It is adaptable for various scenarios, including nuclear accidents and hazardous material transportation.
• The approach supports informed decision-making for land-use planning and emergency procedure assessment.