EMS location-allocation problem under uncertainties

Abstract

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