Ambulance Location Models for Emergency Medical Services: A Survey with Focus on Developing Countries

Abstract: Emergency Medical Service (EMS) systems face critical challenges in several aspects of the decision-making process which usually involves ambulance location and dispatching decisions. This is even more critical in countries or places where resources are even more constrained. This survey reviews the evolution of EMS optimization models, from classical deterministic approaches to contemporary stochastic programming formulations. We examine deterministic models, including the location set covering and maximal covering location models, and probabilistic approaches, including queuing-based models, and stochastic programming models that explicitly handle demand uncertainty. Special attention is given to models developed for Mexican EMS systems, where ambulance availability often falls 30-60% below World Health Organization (WHO) recommendations. We present a detailed comparison of two recent approaches: a maximum expected coverage model that introduces partial coverage through decay functions for heterogeneous ambulance fleets applied in Monterrey, and a robust double standard model applied to Tijuana's Red Cross operations. Our analysis reveals how these models successfully address the unique challenges of developing countries through scenario-based formulations and practical solution methodologies that balance computational tractability with operational realism.