Optimal Transportation of Casualties to Hospitals after Multi-Disaster Mass Casualty Incidents

Abstract Mass casualty incidents (MCIs) resulting from compound disasters are characterized by a sudden surge in casualties that often overwhelms emergency medical services. In such situations, the efficient and coordinated transportation of casualties to hospitals becomes a critical component of the disaster response effort. However, current transportation strategies largely rely on static triage rules (for example, the immediate-first rule) and limited coordination between field, hospitals, and ambulances, which can lead to suboptimal use of scarce resources and to additional loss of life. To address these limitations, this study introduced the dynamic casualty transportation (DCT) model, a novel decision support tool designed to guide real-time transportation decisions during complex emergencies. The model dynamically prioritizes casualty transportation, aiming to maximize the total number of survivors. Unlike rule-based approaches commonly used in practice, DCT adapts to evolving field conditions and simultaneously determines both the optimal assignment of casualties to hospitals and the routing of ambulances. Numerical experiments show that DCT consistently outperforms the immediate-first rule, achieving up to a 59% improvement in survival under high-severity conditions when hospitals are nearby and over 300% when longer ambulance travel times are involved. Sensitivity analyses further confirm the robustness of the model to travel-time estimation errors. To enable practical deployment and support timely, data-driven decisions, we also proposed a real-time implementation architecture that integrates live streams from GPS, traffic-informed mapping services, mobile networks, and electronic triage assessments.