A stochastic optimization model for real-time ambulance redeployment

Abstract

When ambulances are engaged in responding to emergency calls, the ability to respond quickly to future calls is considerably compromised. The available ambulances are typically relocated to reestablish maximal coverage. We present a two-stage stochastic optimization model for the ambulance redeployment problem that minimizes the number of relocations over a planning horizon while maintaining an acceptable service level. We conduct computational testing based on the real historical data from the Region of Waterloo Emergency Medical Services. The results show that the optimal relocation strategies can be computed within 40s of computational time for a desired service level of 90%.