Ambulance location and relocation under budget constraints: investigating coverage-maximization models and ambulance...

Ambulance location in Emergency Medical Services (EMS) is a widely studied problem requiring efficient resource allocation within budgetary constraints. The literature has focused on enhancing EMS performance with limited attention given to their economic performance. This study addresses EMS performance with an emphasis on budget constraints by revising three coverage maximization models: the time-dependent Maximum Expected Coverage Location Problem (time-dependent MEXCLP), the multi-period Double Standard Model (mDSM), and the multi-period Queueing Maximal Availability Location Problem (Q-MALP-M2). These models are adapted to incorporate ambulance types, multi-period relocation, and budget constraints related to costs associated with ambulance station openings, ambulance acquisition, transport, and multi-period relocation. The revised models, along with two hybrid models (model 1 and model 2), were evaluated and compared using a discrete-event simulation model based on three key performance indicators: 1) coverage, 2) waiting time, and 3) time to arrive at the hospital. Additionally, the study investigates ambulance sharing as a policy to enhance EMS performance, wherein a single ambulance serves two patients whenever feasible. The study uses data from the Fez-Meknes region in Morocco, collected in 2021. Results indicate that hybrid model 1 outperformed the other models in most scenarios, as it allows for the decentralization of ambulances by investing the allocated budget in constructing new ambulance stations and acquiring new ambulances, contrasting with the other models that allocate almost the entire budget to purchasing new ambulances. Furthermore, the findings reveal that ambulance sharing significantly improves EMS performance, particularly under tightening budgetary restrictions and increasing demand; however, the benefits of ambulance sharing diminish as the allocated budget increases.