A robust optimization model for allocation-routing problems under uncertain conditions.

Area of Science:

• Operations Research
• Disaster Management
• Public Health

Background:

• Post-earthquake emergency logistics is complex, with challenges in resource allocation, casualty assessment, and time sensitivity.
• Efficient scientific rescue planning is critical, yet integrating medical facility location and casualty allocation remains underexplored.

Purpose of the Study:

• To develop a robust optimization model for locating medical facilities and allocating casualties in a three-level rescue system.
• To minimize the total Trauma Index Score (TIS) of casualties under resource constraints and uncertainty.

Main Methods:

• A robust optimization model integrating facility location and casualty allocation within a three-level rescue chain (disaster areas, temporary hospitals, general hospitals).
• Utilized the Trauma Index Score (TIS) method for casualty classification and considered dynamic injury changes.
• Applied robust optimization to handle uncertainty in casualty numbers and data variability.

Main Results:

• The robust optimization model effectively determines hospital locations and casualty transportation plans, considering data variability and uncertainty budgets.
• Temporary hospital capacity has a greater impact on the objective function than general hospitals.
• The robust model outperforms deterministic models for larger problem sizes, and casualty number uncertainty significantly affects serious casualties.

Conclusions:

• The proposed robust optimization model provides a scientific approach to enhance post-earthquake emergency medical logistics.
• The model's extension into a two-stage dynamic location-allocation framework improves its applicability in complex disaster scenarios.
• Findings highlight the critical role of uncertainty management and temporary hospital capacity in optimizing rescue operations.