Alternate risk measures for emergency medical service system design

The stochastic nature of emergency service requests and the unavailability of emergency vehicles when requested to serve demands are critical issues in constructing valid models representing real life emergency medical service (EMS) systems. We consider an EMS system design problem with stochastic demand and locate the emergency response facilities and vehicles in order to ensure target levels of coverage, which are quantified using risk measures on random unmet demand. The target service levels for each demand site and also for the entire service area are specified. In order to increase the possibility of representing a wider range of risk preferences we develop two stochastic optimization models involving alternate risk measures. Our first model 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 effectiveness.