EMS location-allocation problem under uncertainties

• A location-allocation problem under uncertainty is investigated. two-stage stochastic programming model proposed. The mode solved by sample average approximation. Emergencies, especially those considered routine (i.e., occurring on a daily basis), pose great threats to health, life, and property. Immediate response treatment can greatly mitigate these threats. This research conducted optimize the locations of ambulance stations, deployment ambulances, dispatch vehicles demand traffic uncertainty, which are main factors that influence emergency time. formulated as dynamic scenario-based model, aiming minimize total cost while responding much possible. Sample Average Approximation proposed approximate original using limited number scenarios, two-phase Benders Decomposition solution scheme accelerate computation, when solving large-sized problem. Numerical experiments real-world data validate performance method. results demonstrate effectiveness efficiency algorithm. We additionally conduct sensitivity analysis evaluate influences crucial parameters, including time standard, facility capacity, service heterogeneity. managerial insights derived from will provide valuable guidance for design an system in practice.