IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v65y2014i7p1053-1067.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.palgrave-journals.com/jors/journal/v65/n7/pdf/jors201352a.pdf
    File Function: Link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: http://www.palgrave-journals.com/jors/journal/v65/n7/full/jors201352a.html
    File Function: Link to full text HTML
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:65:y:2014:i:7:p:1053-1067. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.