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A dynamic ambulance management model for rural areas

Author

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  • T. C. Barneveld

    (Centrum Wiskunde & Informatica)

  • S. Bhulai

    (VU University Amsterdam)

  • R. D. Mei

    (Centrum Wiskunde & Informatica)

Abstract

We study the Dynamic Ambulance Management (DAM) problem in which one tries to retain the ability to respond to possible future requests quickly when ambulances become busy. To this end, we need models for relocation actions for idle ambulances that incorporate different performance measures related to response times. We focus on rural regions with a limited number of ambulances. We model the region of interest as an equidistant graph and we take into account the current status of both the system and the ambulances in a state. We do not require ambulances to return to a base station: they are allowed to idle at any node. This brings forth a high degree of complexity of the state space. Therefore, we present a heuristic approach to compute redeployment actions. We construct several scenarios that may occur one time-step later and combine these scenarios with each feasible action to obtain a classification of actions. We show that on most performance indicators, the heuristic policy significantly outperforms the classical compliance table policy often used in practice.

Suggested Citation

  • T. C. Barneveld & S. Bhulai & R. D. Mei, 2017. "A dynamic ambulance management model for rural areas," Health Care Management Science, Springer, vol. 20(2), pages 165-186, June.
    • Handle: RePEc:kap:hcarem:v:20:y:2017:i:2:d:10.1007_s10729-015-9341-3
    DOI: 10.1007/s10729-015-9341-3
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    References listed on IDEAS

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    2. 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.
    3. 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.
    4. Ľuboš Buzna & Peter Czimmermann, 2021. "On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia," Mathematics, MDPI, vol. 9(17), pages 1-30, September.
    5. Drent, Collin & Keizer, Minou Olde & Houtum, Geert-Jan van, 2020. "Dynamic dispatching and repositioning policies for fast-response service networks," European Journal of Operational Research, Elsevier, vol. 285(2), pages 583-598.
    6. Mark Brennan & Sophia Dyer & Jonas Jonasson & James Salvia & Laura Segal & Erin Serino & Justin Steil, 2024. "The policy case for designating EMS teams for vulnerable patient populations: Evidence from an intervention in Boston," Health Care Management Science, Springer, vol. 27(1), pages 72-87, March.

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