Abstract
Emergency medical service (EMS) systems aim to respond to emergency calls and provide life-saving care to patients. The location of EMS resources is critical to providing this care in a timely manner, and as a result, EMS facility location problems have received a tremendous amount of attention since the 1960s, and their advancement is directly tied to a wide range of facility location problems. This chapter reviews uncertainty in facility location problems applied to EMS systems and provides an intuition for and understanding of EMS problem settings. The chapter begins by explaining EMS response processes and the goals of the early deterministic models. Next, it introduces probabilistic formulations that account for uncertainty in ambulance availability, response time, and demand. Then, it highlights directions within the field and the role of uncertainty in these problem settings. This includes EMS systems with tiered units, systems that consider resource relocation, EMS systems in developing countries, and several other areas. Lastly, it concludes by providing insights into how these models are used in practice.
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Acknowledgements
The authors acknowledge the Mecklenburg EMS Agency and NEMSIS for data used in this chapter. The content reproduced from the NEMSIS Database remains the property of the National Highway Traffic Safety Administration. The National Highway Traffic Safety Administration is not responsible for any claims arising from works based on the original data, text, tables, or figures. The third author was in part supported by the National Science Foundation Award 2000986. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US Government or the National Science Foundation.
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Stratman, E.G., Boutilier, J.J., Albert, L.A. (2023). Uncertainty in Facility Location Models for Emergency Medical Services. In: Eiselt, H.A., Marianov, V. (eds) Uncertainty in Facility Location Problems. International Series in Operations Research & Management Science, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-031-32338-6_9
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