A data-driven districting to improve emergency medical service systems

Emergency Medical Services (EMS) are responsible of providing medical assistance to any person who requires it in case of emergency, with the main goal of reducing the response time. To do so, several management strategies have been proposed, e.g., districting and preassigned dispatching lists. However, districts are often a priori defined at the strategical or tactical level, without considering the dynamics of service provisioning in detail. This may create problems at the operational level, e.g., the need to send ambulances out of their district to compensate local criticalities and keep a good service quality. To overcome this problem, we propose a data-driven districting that minimizes out-of-district assignments while compacting the districts and balancing their workloads. The approach consists of an iterative matheuristic in which, at each iteration, we first solve the operational problem (location and allocation of ambulances) for a given districting, and then we redistrict based on the operational solution. To validate our proposal, we apply the approach considering realistic data from the city of Montreal, Canada, working on a small region with 30 demand zones extracted from the entire city. Results show that the approach generates balanced districts in which the ambulances satisfy the emergency calls within a low response time. Results also suggest that the increment of the response time to keep the workloads balanced is limited.