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Simulation and Real-Time Optimised Relocation for Improving Ambulance Operations

In: Handbook of Healthcare Operations Management

Author

Listed:
  • Andrew James Mason

    (University of Auckland)

Abstract

In this chapter we discuss operations research models and methods for simulating and optimizing ambulance operations. We also discuss our experiences in developing and applying software that implements these techniques. We describe a new simulation-optimization algorithm for base location. We also present a case study detailing how the software we developed was used as part of a major reorganisation of ambulance operations in Copenhagen, Denmark. This chapter also examines the complex problem of real-time ambulance relocation. We review the literature in this area, and describe a new real-time ambulance re-positioning optimisation model and associated software now being used by ambulance operators in several countries to improve their operations.

Suggested Citation

  • Andrew James Mason, 2013. "Simulation and Real-Time Optimised Relocation for Improving Ambulance Operations," International Series in Operations Research & Management Science, in: Brian T. Denton (ed.), Handbook of Healthcare Operations Management, edition 127, chapter 0, pages 289-317, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-5885-2_11
    DOI: 10.1007/978-1-4614-5885-2_11
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Matthew S. Maxwell & Eric Cao Ni & Chaoxu Tong & Shane G. Henderson & Huseyin Topaloglu & Susan R. Hunter, 2014. "A Bound on the Performance of an Optimal Ambulance Redeployment Policy," Operations Research, INFORMS, vol. 62(5), pages 1014-1027, October.
    4. 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.
    5. Martin van Buuren & Caroline Jagtenberg & Thije van Barneveld & Rob van der Mei & Sandjai Bhulai, 2018. "Ambulance Dispatch Center Pilots Proactive Relocation Policies to Enhance Effectiveness," Interfaces, INFORMS, vol. 48(3), pages 235-246, June.
    6. Amir Ali Nasrollahzadeh & Amin Khademi & Maria E. Mayorga, 2018. "Real-Time Ambulance Dispatching and Relocation," Manufacturing & Service Operations Management, INFORMS, vol. 20(3), pages 467-480, July.
    7. Amir Rastpour & Armann Ingolfsson & Bora Kolfal, 2020. "Modeling Yellow and Red Alert Durations for Ambulance Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1972-1991, August.
    8. 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.
    9. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2631-2689, September.
    10. Mengyu Li & Peter Vanberkel & Alix J. E. Carter, 2019. "A review on ambulance offload delay literature," Health Care Management Science, Springer, vol. 22(4), pages 658-675, December.

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