Fairness in ambulance routing for post disaster management.

Area of Science:

• Operations Research
• Disaster Management
• Public Health Logistics

Background:

• Disaster management response phase relies heavily on humanitarian logistics for efficient relief operations.
• The ambulance routing problem (ARP) is critical for transporting injured individuals to hospitals post-disaster.
• Ensuring equity and fairness in emergency medical services is paramount, especially during disaster aftermath.

Purpose of the Study:

• To develop an optimized ambulance routing strategy for disaster relief operations.
• To address the ambulance routing problem by incorporating fairness and equity alongside efficiency.
• To formulate a novel variant of the team orienteering problem with hierarchical objectives.

Main Methods:

• Formulation of a new team orienteering problem variant with hierarchical objectives.
• Development of a hybrid algorithm combining machine learning and neighborhood search.
• Creation and utilization of a new set of realistic benchmark instances for quantitative analysis.

Main Results:

• The proposed hybrid algorithm significantly reduces solution running time, particularly for complex scenarios.
• Demonstrated capability to find efficient ambulance tours while considering fairness and equity.
• Quantitative analysis validated the algorithm's performance against benchmark instances.

Conclusions:

• The developed hybrid algorithm offers an effective solution for optimizing ambulance routing in disaster scenarios.
• Balancing efficiency and equity in humanitarian logistics is achievable through advanced optimization techniques.
• The research provides a valuable tool for improving emergency medical response during crises.