Emergency Medical Service System Design with Risk Measures

Abstract

The stochastic nature of emergency service requests and the unavailability of emergency vehicles (ambulances) when requested to serve demands are critical issues in constructing valid models representing real life emergency medical service (EMS) systems. Such stochastic emergency facility location models has received significant attention in the last decades. In this paper, the EMS system design problem involves decisions on where each emergency response facility (ambulance station) should be located and how many vehicles should be assigned to each facility. Unlike the existing models in the literature, we locate the facilities and decide on their capacities in such a way as to ensure a reliable level of coverage, which is quantified using risk measures on random total unmet demand. We specify reliability levels of service for each demand site and also for the entire service area. We develop two stochastic optimization models under demand uncertainty; the first one includes integrated chance constraints (ICCs), whereas the second one incorporates ICCs and a second order dominance constraint. We propose solution methods for our stochastic optimization problems and present extensive numerical results demonstrating their computational eectiveness.