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A stochastic approach for designing two-tiered emergency medical service systems

Author

Listed:
  • Rania Boujemaa

    (Université de Tunis El Manar)

  • Aida Jebali

    (University of Sharjah)

  • Sondes Hammami

    (Université de Tunis El Manar
    Université de Carthage)

  • Angel Ruiz

    (Université Laval)

  • Hanen Bouchriha

    (Université de Tunis El Manar)

Abstract

Emergency medical services (EMS) systems provide out-of-hospital acute medical care and transportation to the appropriate health care provider to patients with illnesses and injuries. The objective of EMS systems is to satisfy demand requests by providing timely first care medical assistance to patients at the incident scene. This paper aims at designing a robust two-tiered EMS system while accounting for the inherent uncertainty of the demand. A two-stage stochastic programming location-allocation model is proposed to simultaneously determine the location of ambulance stations, the number and the type of ambulances to be deployed, and the demand areas served by each station. This problem is then solved efficiently using the sampling average approximation algorithm. Computational experiments highlight the performance of the proposed solution approach and its practical applicability.

Suggested Citation

  • Rania Boujemaa & Aida Jebali & Sondes Hammami & Angel Ruiz & Hanen Bouchriha, 2018. "A stochastic approach for designing two-tiered emergency medical service systems," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 123-152, June.
    • Handle: RePEc:spr:flsman:v:30:y:2018:i:1:d:10.1007_s10696-017-9286-6
    DOI: 10.1007/s10696-017-9286-6
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    Cited by:

    1. Sondes Hammami & Aida Jebali, 2021. "Designing modular capacitated emergency medical service using information on ambulance trip," Operational Research, Springer, vol. 21(3), pages 1723-1742, September.
    2. Wang, Wei & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2022. "EMS location-allocation problem under uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    3. Harrou, Fouzi & Dairi, Abdelkader & Kadri, Farid & Sun, Ying, 2020. "Forecasting emergency department overcrowding: A deep learning framework," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. 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.
    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.

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