Computational Comparison of Five Maximal Covering Models for Locating Ambulances

Abstract

This article categorizes existing maximum coverage optimization models for locating ambulances based on whether the models incorporate uncertainty about (1) ambulance availability and (2) response times. Data from Edmonton, Alberta, Canada are used to test five different models, using the approximate hypercube model to compare solution quality between models. The basic maximum covering model, which ignores these two sources of uncertainty, generates solutions that perform far worse than those generated by more sophisticated models. For a specified number of ambulances, a model that incorporates both sources of uncertainty generates a configuration that covers up to 26% more of the demand than the configuration produced by the basic model.