Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data

This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergen-cy medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering loca-tion problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambu-lances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute cov-erage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particu-larly in situations where time is of the essence.