A bi-objective robust optimization model for location-transportation under uncertainty with psychological costs

Area of Science:

• Operations Research
• Disaster Management
• Public Health

Background:

• Earthquake rescue logistics face significant uncertainty due to unpredictable casualty numbers and psychological trauma.
• Effective emergency response requires integrating casualty psychological conditions into rescue strategies.

Purpose of the Study:

• To develop a bi-objective robust optimization model for optimal medical facility location and casualty transportation planning.
• To minimize total Injury Severity Score (ISS) and psychological costs under uncertainty.

Main Methods:

• A three-tier rescue chain model (disaster areas, temporary hospitals, comprehensive hospitals) was developed.
• Robust optimization and the [Formula: see text] constraint method were used to solve the bi-objective stochastic model.
• Casualty classification using ISS and dynamic deterioration rates were incorporated.

Main Results:

• Increased uncertainty in casualty numbers significantly impacts total ISS.
• Addressing psychological conditions improves humanitarian care but may decrease rescue efficiency.
• Prioritizing severe casualties and expanding temporary hospital capacity enhances rescue efficiency.

Conclusions:

• Robust optimization models outperform deterministic models for large-scale disaster scenarios.
• Decision-makers must balance humanitarian care with rescue efficiency based on specific preferences.
• The model provides a framework for optimizing medical resource allocation in post-earthquake scenarios.