EMS location-allocation problem under uncertainties

Abstract

Emergency especially routine emergency that happens on a daily basis poses great threat to our health, life, and property. Immediate response and treatment can greatly mitigate these threats. This research optimizes location of ambulance stations, deployment of ambulances, and vehicle dispatching under demand and traffic uncertainty, which are the main factors that influence response time. The problem is formulated as a dynamic scenario-based two-stage stochastic programming model with the aim of minimizing total cost under service level requirements and is solved by Sample Average Approximation. Finally, we conduct numerical experiments using real-world emergency data to evaluate the performance of our methods, which yields valuable managerial insights for the design of EMS response system.