Purpose: To formulate and solve a mathematical model optimizing the allocation of patients to hospitals following a mass casualty event (MCE), and to use the results of this model develop operational guidelines and benchmarks to improve the performance of emergency medical services (EMS) dispatch.

Methods: We developed a mixed-integer programming (MIP) model for this dynamic ambulance dispatching problem. The objective is to minimize the total time, including transportation time, waiting time, and treatment time required for patients in an MCE. We created a separate simulation model to verify the results of the optimization model using a case study of 150 patients, 10 hospitals, and 50 ambulances.

Results: An effective heuristic algorithm was designed by assigning subset of patients to a given hospital or specified number of ambulance trips with the intention of reducing total completion time. In the case study, the overall completion time obtained using the heuristic algorithm is 262 minutes, considerably smaller than what obtained from the simulation (354 minutes) which does not consider the sequence of ambulance movement.

Conclusion: Quantitative planning models that can determine optimal ambulance routings in terms of total finishing time in an MCE can provide operational guidelines for regional emergency planning, as well as strategic evaluation of disaster preparedness plans. Optimizing ambulance routing in an MCE may significantly reduce patient waiting time.