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A Bound on the Performance of an Optimal Ambulance Redeployment Policy

Author

Listed:
  • Matthew S. Maxwell

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

  • Eric Cao Ni

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

  • Chaoxu Tong

    (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)

  • Huseyin Topaloglu

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

  • Susan R. Hunter

    (School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907)

Abstract

Ambulance redeployment is the practice of repositioning ambulance fleets in real time in an attempt to reduce response times to future calls. When redeployment decisions are based on real-time information on the status and location of ambulances, the process is called system-status management. An important performance measure is the long-run fraction of calls with response times over some time threshold. We construct a lower bound on this performance measure that holds for nearly any ambulance redeployment policy through comparison methods for queues. The computation of the bound involves solving a number of integer programs and then simulating a multiserver queue. This work originated when one of the authors was asked to analyze a response to a request-for-proposals (RFP) for ambulance services in a county in North America.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:62:y:2014:i:5:p:1014-1027
    DOI: 10.1287/opre.2014.1302
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    References listed on IDEAS

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    Cited by:

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    3. Enayati, Shakiba & Mayorga, Maria E. & Rajagopalan, Hari K. & Saydam, Cem, 2018. "Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers," Omega, Elsevier, vol. 79(C), pages 67-80.
    4. 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.
    5. 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.
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    7. 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.
    8. 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.
    9. Jagtenberg, C.J. & van den Berg, P.L. & van der Mei, R.D., 2017. "Benchmarking online dispatch algorithms for Emergency Medical Services," European Journal of Operational Research, Elsevier, vol. 258(2), pages 715-725.
    10. Saif Benjaafar & Daniel Jiang & Xiang Li & Xiaobo Li, 2022. "Dynamic Inventory Repositioning in On-Demand Rental Networks," Management Science, INFORMS, vol. 68(11), pages 7861-7878, November.
    11. Bertsimas, Dimitris & Ng, Yeesian, 2019. "Robust and stochastic formulations for ambulance deployment and dispatch," European Journal of Operational Research, Elsevier, vol. 279(2), pages 557-571.
    12. 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).
    13. 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.
    14. 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.
    15. Khaled Abdelghany & Parya Roustaee & Ahmed Hassan & Aline Karak & Mohammad Khodayar, 2023. "Equilibrium-based Workload Balancing for Robust Emergency Response Operation," Networks and Spatial Economics, Springer, vol. 23(3), pages 715-753, September.

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