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Determining optimal treatment rate after a disaster

Author

Listed:
  • Asli Kilic

    (Ege University, Department of Statistics, Bornova-Izmir, Turkey)

  • M Cemali Dincer

    (Izmir University of Economics, Department of Industrial Systems Engineering, Balcova-Izmir, Turkey)

  • Mahmut Ali Gokce

    (Izmir University of Economics, Department of Industrial Systems Engineering, Balcova-Izmir, Turkey)

Abstract

From the standpoint of medical services, a disaster is a calamitous event resulting in an unexpected number of casualties that exceeds the therapeutic capacities of medical services. In these situations, effective medical response plays a crucial role in saving life. To model medical rescue activities, a two-priority non-preemptive S-server, and a finite capacity queueing system is considered. After constructing Chapman–Kolmogorov differential equations, Pontryagin's minimum principle is used to calculate optimal treatment rates for each priority class. The performance criterion is to minimize both the expected value of the square of the difference between the number of servers and the number of patients in the system, and also the cost of serving these patients over a determined time period. The performance criterion also includes a final time cost related to deviations from the determined value of the desired queue length. The two point boundary value problem is numerically solved for different arrival rate patterns and selected parameters.

Suggested Citation

  • Asli Kilic & M Cemali Dincer & Mahmut Ali Gokce, 2014. "Determining optimal treatment rate after a disaster," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(7), pages 1053-1067, July.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:7:p:1053-1067
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    Cited by:

    1. Alizadeh, Morteza & Amiri-Aref, Mehdi & Mustafee, Navonil & Matilal, Sumohon, 2019. "A robust stochastic Casualty Collection Points location problem," European Journal of Operational Research, Elsevier, vol. 279(3), pages 965-983.
    2. Emmett J. Lodree & Nezih Altay & Robert A. Cook, 2019. "Staff assignment policies for a mass casualty event queuing network," Annals of Operations Research, Springer, vol. 283(1), pages 411-442, December.
    3. Aakil M. Caunhye & Xiaofeng Nie, 2018. "A Stochastic Programming Model for Casualty Response Planning During Catastrophic Health Events," Transportation Science, INFORMS, vol. 52(2), pages 437-453, March.
    4. Lee, Hyun-Rok & Lee, Taesik, 2021. "Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response," European Journal of Operational Research, Elsevier, vol. 291(1), pages 296-308.
    5. Caunhye, Aakil M. & Li, Mingzhe & Nie, Xiaofeng, 2015. "A location-allocation model for casualty response planning during catastrophic radiological incidents," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 32-44.

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