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A generic and flexible simulation-based analysis tool for EMS management

Author

Listed:
  • Y. Kergosien
  • V. Bélanger
  • P. Soriano
  • M. Gendreau
  • A. Ruiz

Abstract

Emergency medical services (EMS) are dedicated to provide urgent medical care to any person requiring it and to ensure their transport to a hospital or care facility, if required. Moreover, in many contexts, EMS also have to provide transportation services for patients need to go from one hospital to another or between their home and the hospital. For such organisations, efficient strategies for managing the ambulance fleet at their disposal have to be selected, but the highly random and dynamic nature of the system under study makes this a challenging task. Most of the published studies which have considered these issues have done it focusing on a specific EMS context, one city or one territory for instance. However, it is possible to identify several common characteristics and processes from one EMS context to another. This is the purpose of the generic discrete event simulation-based analysis tool proposed here, which can be adapted to a wide range of EMS contexts. In particular, it explicitly considers the two types of tasks that can compose the mission of an EMS: serving emergency requests and providing transports between care units/hospitals/patients’ homes.

Suggested Citation

  • Y. Kergosien & V. Bélanger & P. Soriano & M. Gendreau & A. Ruiz, 2015. "A generic and flexible simulation-based analysis tool for EMS management," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7299-7316, December.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:24:p:7299-7316
    DOI: 10.1080/00207543.2015.1037405
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    Cited by:

    1. Chang, Kuo-Hao & Chen, Tzu-Li & Yang, Fu-Hao & Chang, Tzu-Yin, 2023. "Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1237-1262.
    2. Lee, Yu-Ching & Chen, Yu-Shih & Chen, Albert Y., 2022. "Lagrangian dual decomposition for the ambulance relocation and routing considering stochastic demand with the truncated Poisson," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 1-23.
    3. Ridler, Samuel & Mason, Andrew J. & Raith, Andrea, 2022. "A simulation and optimisation package for emergency medical services," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1101-1113.
    4. Reuter-Oppermann, Melanie & Wolff, Clemens & Pumplun, Luisa, 2021. "Next Frontiers in Emergency Medical Services in Germany: Identifying Gaps between Academia and Practice," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124665, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. 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.
    6. Han, Jialin & Hu, Yaoguang & Mao, Mingsong & Wan, Shuping, 2020. "A multi-objective districting problem applied to agricultural machinery maintenance service network," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1120-1130.
    7. 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.
    8. Carvalho, A.S. & Captivo, M.E. & Marques, I., 2020. "Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1064-1080.

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