Designing robust emergency medical service via stochastic programming

This paper addresses the problem of designing robust emergency medical services. Under this respect, the main issue to consider is the inherent uncertainty which characterizes real life situations. Several approaches can be used to design robust mathematical models which are able to hedge uncertain conditions. We are using here the stochastic programming framework and, in particular, the probabilistic paradigm. More specifically, we develop a stochastic programming model with probabilistic constraints aimed to solve both the location and the dimensioning problems, i.e. where service sites must be located and how many emergency vehicles must be assigned to each site, in order to achieve a reliable level of service and minimize the overall costs. In doing so, we consider the randomness of the system as far as the demand of emergency service is concerned. The numerical results, which have been collected on a large set of test problems, demonstrate the validity of the proposed model, particularly in dealing with the trade-off between quality of service and costs management.