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Reducing disparities in large-scale emergency medical service systems

Author

Listed:
  • Hector Toro-Díaz

    (Clemson University, Clemson, SC, USA)

  • Maria E Mayorga

    (NC State University, Raleigh, NC, USA)

  • Laura A McLay

    (University of Wisconsin, Madison, WI, USA)

  • Hari K Rajagopalan

    (Francis Marion University, Florence, SC, USA)

  • Cem Saydam

    (University of North Carolina, Charlotte, NC, USA)

Abstract

Emergency Medical Service (EMS) systems operate under the pressure of knowing that human lives might be directly at stake. In the public eye there is a natural expectation of efficient response. There is abundant literature on the topic of efficient planning of EMS systems (maximizing expected coverage or minimizing response time). Other objectives have been considered but the literature available is very sparse compared to efficiency-based works. Furthermore, while real size EMS systems have been studied, the use of exact models is usually hindered by the amount of computational time required to obtain solutions. We approach the planning of large-scale EMS systems including fairness considerations using a Tabu Search-based heuristic with an embedded approximation procedure for the queuing submodel. This allows for the analysis of large-scale real systems, extending the approach in which strategic decisions (location) and operative decisions (dispatching) are combined to balance efficiency and fairness.

Suggested Citation

  • Hector Toro-Díaz & Maria E Mayorga & Laura A McLay & Hari K Rajagopalan & Cem Saydam, 2015. "Reducing disparities in large-scale emergency medical service systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(7), pages 1169-1181, July.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:7:p:1169-1181
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    Citations

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    Cited by:

    1. 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.
    2. Iannoni, Ana P. & Morabito, Reinaldo, 2023. "A review on hypercube queuing model's extensions for practical applications," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    3. 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.
    4. Luo, Weicong & Yao, Jing & Mitchell, Richard & Zhang, Xiaoxiang & Li, Wenqiang, 2022. "Locating emergency medical services to reduce urban-rural inequalities," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    5. 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.
    6. L’udmila Jánošíková & Peter Jankovič & Marek Kvet & Gaston Ivanov & Jakub Holod & Imrich Berta, 2022. "Reorganization of an Emergency Medical System in a Mixed Urban-Rural Area," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    7. Akdogan, M. Altan & Bayındır, Z. Pelin & Iyigun, Cem, 2023. "An analysis of ambulance location problem from an equity perspective," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).

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