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The Vehicle Mix Decision in Emergency Medical Service Systems

Author

Listed:
  • Kenneth C. Chong

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Shane G. Henderson

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Mark E. Lewis

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

Abstract
We consider the problem of selecting the number of advanced life support (ALS) and basic life support (BLS) ambulances—the vehicle mix —to deploy in an emergency medical service (EMS) system, given a budget constraint. ALS ambulances can treat a wider range of emergencies, whereas BLS ambulances are less expensive to operate. To this end, we develop a framework under which the performance of a system operating under a given vehicle mix can be evaluated. Because the choice of vehicle mix affects how ambulances are dispatched to incoming calls, as well as how they are deployed to base locations, we adopt an optimization-based approach. We construct two models—one a Markov decision process, the other an integer program—to study the problems of dispatching and deployment in a tiered system, respectively. In each case, the objective function value attained by an optimal decision serves as our performance measure. Numerical experiments performed with both models suggest that, under reasonable choices of inputs, a wide range of tiered systems perform comparably to all-ALS fleets.

Suggested Citation

  • Kenneth C. Chong & Shane G. Henderson & Mark E. Lewis, 2016. "The Vehicle Mix Decision in Emergency Medical Service Systems," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 347-360, July.
    • Handle: RePEc:inm:ormsom:v:18:y:2016:i:3:p:347-360
    DOI: 10.1287/msom.2015.0555
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    References listed on IDEAS

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    3. 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.
    4. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    5. Shuangchi He & Melvyn Sim & Meilin Zhang, 2019. "Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach," Management Science, INFORMS, vol. 65(9), pages 4123-4140, September.
    6. Nelas, José & Dias, Joana, 2020. "Optimal Emergency Vehicles Location: An approach considering the hierarchy and substitutability of resources," European Journal of Operational Research, Elsevier, vol. 287(2), pages 583-599.

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