Emergency Rescue Medical Evacuation Scheduling Strategy Based on Time–Space Network and Branch-and-Price Algorithm

Abstract

In major disaster-induced mass casualty events, medical evacuation (MEDEVAC) serves as a key factor in determining casualty survival rates. To overcome the limitations of prior studies—particularly in constraint quantification and practical dispatching — we propose a multi-commodity flow model for MEDEVAC based on a time–space network. Using a state– time expanded network, our model integrates injury severity levels, time windows, asset capacity and type, facility heterogeneity, and route dependencies. We formulate a mixed-integer linear programming (MILP) model and develop a rolling-horizon branch-and-price algorithm. Case study results show an evacuation rate of 81.38% and an average waiting time of 77.81 minutes, demonstrating improved efficiency and resource utilization. This serves as a theoretical foundation for quantitative decision-making and dynamic scheduling of medical evacuation in major disasters.