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Locating emergency vehicles: Modelling the substitutability of resources and the impact of delays in the arrival of assistance

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  • Nelas, José
  • Dias, Joana

Abstract

The quality and promptness of emergency assistance is highly dependant on the location of existing emergency vehicles. In this work, we propose a new model for optimizing emergency vehicles’ location that takes into account the existence of different types of emergency vehicles and the level of care they can provide, the possibility of vehicles’ substitution considering the hierarchy of levels of care and the explicit consideration of the progression of an emergency episode when the arrival of assistance suffers delays. The inherent uncertainty that exists in this problem is represented by a set of scenarios. A heuristic procedure for solving the problem was also developed. The model and algorithmic approach were tested using real data. It is possible to conclude that the application of stochastic location models that explicitly consider the evolution of the health condition of the victims when care is delayed can lead to better emergency coverage. The location of vehicles is indeed influenced by the explicit consideration of the impact of assistance time on the victims’ conditions.

Suggested Citation

  • Nelas, José & Dias, Joana, 2021. "Locating emergency vehicles: Modelling the substitutability of resources and the impact of delays in the arrival of assistance," Operations Research Perspectives, Elsevier, vol. 8(C).
    • Handle: RePEc:eee:oprepe:v:8:y:2021:i:c:s221471602100021x
    DOI: 10.1016/j.orp.2021.100202
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    References listed on IDEAS

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    1. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    2. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    3. Soovin Yoon & Laura A. Albert & Veronica M. White, 2021. "A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances," Transportation Science, INFORMS, vol. 55(2), pages 275-296, March.
    4. Bertsimas, Dimitris & Ng, Yeesian, 2019. "Robust and stochastic formulations for ambulance deployment and dispatch," European Journal of Operational Research, Elsevier, vol. 279(2), pages 557-571.
    5. Sunarin Chanta & Maria Mayorga & Laura McLay, 2014. "Improving emergency service in rural areas: a bi-objective covering location model for EMS systems," Annals of Operations Research, Springer, vol. 221(1), pages 133-159, October.
    6. Nelas, José & Dias, Joana, 2020. "Optimal Emergency Vehicles Location: An approach considering the hierarchy and substitutability of resources," European Journal of Operational Research, Elsevier, vol. 287(2), pages 583-599.
    7. Matthew S. Maxwell & Mateo Restrepo & Shane G. Henderson & Huseyin Topaloglu, 2010. "Approximate Dynamic Programming for Ambulance Redeployment," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 266-281, May.
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    Cited by:

    1. Youness Frichi & Lina Aboueljinane & Fouad Jawab, 2025. "Ambulance location and relocation under budget constraints: investigating coverage-maximization models and ambulance sharing to improve emergency medical services performance," Health Care Management Science, Springer, vol. 28(2), pages 274-297, June.

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