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Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil

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  • de Souza, Regiane Máximo
  • Morabito, Reinaldo
  • Chiyoshi, Fernando Y.
  • Iannoni, Ana Paula

Abstract

Emergency medical services (EMS) assist different classes of patients according to their medical seriousness. In this study, we extended the well-known hypercube model, based on the theory of spatially distributed queues, to analyze systems with multiple priority classes and a queue for waiting customers. Then, we analyzed the computational results obtained when applying this approach to a case study from an urban EMS in the city of Ribeirão Preto, Brazil. We also investigated some scenarios for this system studying different periods of the day and the impact of increasing the demands of the patient classes. The results showed that relevant performance measures can be obtained to analyze such a system by using the analytical model extended to deal with queuing priority. In particular, it can accurately evaluate the average response time for each class of emergency calls individually, paying particular attention to high priority calls.

Suggested Citation

  • de Souza, Regiane Máximo & Morabito, Reinaldo & Chiyoshi, Fernando Y. & Iannoni, Ana Paula, 2015. "Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil," European Journal of Operational Research, Elsevier, vol. 242(1), pages 274-285.
    • Handle: RePEc:eee:ejores:v:242:y:2015:i:1:p:274-285
    DOI: 10.1016/j.ejor.2014.09.056
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    References listed on IDEAS

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    Cited by:

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    2. Rautenstrauss, Maximiliane & Martin, Layla & Minner, Stefan, 2023. "Ambulance dispatching during a pandemic: Tradeoffs of categorizing patients and allocating ambulances," European Journal of Operational Research, Elsevier, vol. 304(1), pages 239-254.
    3. Liu, Han & Hua, Cheng & Lei, Chao, 2021. "Planning for time-varying volunteer firefighter systems under probabilistic service disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    4. 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).
    5. Akbar Karimi & Michel Gendreau & Vedat Verter, 2018. "Performance Approximation of Emergency Service Systems with Priorities and Partial Backups," Transportation Science, INFORMS, vol. 52(5), pages 1235-1252, October.
    6. Soovin Yoon & Laura A. Albert, 2018. "An expected coverage model with a cutoff priority queue," Health Care Management Science, Springer, vol. 21(4), pages 517-533, December.
    7. Liu, Kanglin & Li, Qiaofeng & Zhang, Zhi-Hai, 2019. "Distributionally robust optimization of an emergency medical service station location and sizing problem with joint chance constraints," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 79-101.
    8. Yoon, Soovin & Albert, Laura A., 2021. "Dynamic dispatch policies for emergency response with multiple types of vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    9. Ansari, Sardar & Yoon, Soovin & Albert, Laura A., 2017. "An approximate hypercube model for public service systems with co-located servers and multiple response," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 143-157.
    10. Horváth, Gábor, 2015. "Efficient analysis of the MMAP[K]/PH[K]/1 priority queue," European Journal of Operational Research, Elsevier, vol. 246(1), pages 128-139.
    11. Caio Vitor Beojone & Regiane Máximo de Souza & Ana Paula Iannoni, 2021. "An Efficient Exact Hypercube Model with Fully Dedicated Servers," Transportation Science, INFORMS, vol. 55(1), pages 222-237, 1-2.

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