IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v271y2018i2d10.1007_s10479-018-2760-z.html
   My bibliography  Save this article

Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system

Author

Listed:
  • Phillip R. Jenkins

    (Air Force Institute of Technology)

  • Matthew J. Robbins

    (Air Force Institute of Technology)

  • Brian J. Lunday

    (Air Force Institute of Technology)

Abstract

Military medical planners must develop dispatching policies that dictate how aerial medical evacuation (MEDEVAC) units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on a request’s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic policies. Two computational experiments are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system performance measure. Several excursions are examined to identify how the 9-line MEDEVAC request arrival rate and the MEDEVAC flight speeds impact the optimal dispatching policy. Results indicate that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the linear programming algorithms executed by CPLEX 12.6 with regard to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems.

Suggested Citation

  • Phillip R. Jenkins & Matthew J. Robbins & Brian J. Lunday, 2018. "Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system," Annals of Operations Research, Springer, vol. 271(2), pages 641-678, December.
    • Handle: RePEc:spr:annopr:v:271:y:2018:i:2:d:10.1007_s10479-018-2760-z
    DOI: 10.1007/s10479-018-2760-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-2760-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-018-2760-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. Shaler Stidham, 2002. "Analysis, Design, and Control of Queueing Systems," Operations Research, INFORMS, vol. 50(1), pages 197-216, February.
    2. Damitha Bandara & Maria E Mayorga & Laura A McLay, 2014. "Priority dispatching strategies for EMS systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(4), pages 572-587, April.
    3. Laura McLay & Maria Mayorga, 2010. "Evaluating emergency medical service performance measures," Health Care Management Science, Springer, vol. 13(2), pages 124-136, June.
    4. Damitha Bandara & Maria E. Mayorga & Laura A. McLay, 2012. "Optimal dispatching strategies for emergency vehicles to increase patient survivability," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 15(2), pages 195-214.
    5. J. P. Jarvis, 1985. "Approximating the Equilibrium Behavior of Multi-Server Loss Systems," Management Science, INFORMS, vol. 31(2), pages 235-239, February.
    6. Linda V. Green & Peter J. Kolesar, 2004. "ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science," Management Science, INFORMS, vol. 50(8), pages 1001-1014, August.
    7. Matthew S. Maxwell & Mateo Restrepo & Shane G. Henderson & Huseyin Topaloglu, 2010. "Approximate Dynamic Programming for Ambulance Redeployment," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 266-281, May.
    8. Laura McLay & Maria Mayorga, 2013. "A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities," IISE Transactions, Taylor & Francis Journals, vol. 45(1), pages 1-24.
    9. Rettke, Aaron J. & Robbins, Matthew J. & Lunday, Brian J., 2016. "Approximate dynamic programming for the dispatch of military medical evacuation assets," European Journal of Operational Research, Elsevier, vol. 254(3), pages 824-839.
    10. Grace M. Carter & Jan M. Chaiken & Edward Ignall, 1972. "Response Areas for Two Emergency Units," Operations Research, INFORMS, vol. 20(3), pages 571-594, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Phillip R. Jenkins & Matthew J. Robbins & Brian J. Lunday, 2021. "Approximate Dynamic Programming for Military Medical Evacuation Dispatching Policies," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 2-26, January.
    2. Jenkins, Phillip R. & Robbins, Matthew J. & Lunday, Brian J., 2021. "Approximate dynamic programming for the military aeromedical evacuation dispatching, preemption-rerouting, and redeployment problem," European Journal of Operational Research, Elsevier, vol. 290(1), pages 132-143.
    3. Jenkins, Phillip R. & Lunday, Brian J. & Robbins, Matthew J., 2020. "Robust, multi-objective optimization for the military medical evacuation location-allocation problem," Omega, Elsevier, vol. 97(C).
    4. Robbins, Matthew J. & Jenkins, Phillip R. & Bastian, Nathaniel D. & Lunday, Brian J., 2020. "Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation," Omega, Elsevier, vol. 91(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rettke, Aaron J. & Robbins, Matthew J. & Lunday, Brian J., 2016. "Approximate dynamic programming for the dispatch of military medical evacuation assets," European Journal of Operational Research, Elsevier, vol. 254(3), pages 824-839.
    2. 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.
    3. Phillip R. Jenkins & Matthew J. Robbins & Brian J. Lunday, 2021. "Approximate Dynamic Programming for Military Medical Evacuation Dispatching Policies," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 2-26, January.
    4. 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.
    5. 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).
    6. Ibrahim Çapar & Sharif H Melouk & Burcu B Keskin, 2017. "Alternative metrics to measure EMS system performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 792-808, July.
    7. McCormack, Richard & Coates, Graham, 2015. "A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival," European Journal of Operational Research, Elsevier, vol. 247(1), pages 294-309.
    8. C. J. Jagtenberg & S. Bhulai & R. D. Mei, 2017. "Dynamic ambulance dispatching: is the closest-idle policy always optimal?," Health Care Management Science, Springer, vol. 20(4), pages 517-531, December.
    9. 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.
    10. Robbins, Matthew J. & Jenkins, Phillip R. & Bastian, Nathaniel D. & Lunday, Brian J., 2020. "Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation," Omega, Elsevier, vol. 91(C).
    11. Li, Mengyu & Carter, Alix & Goldstein, Judah & Hawco, Terence & Jensen, Jan & Vanberkel, Peter, 2021. "Determining ambulance destinations when facing offload delays using a Markov decision process," Omega, Elsevier, vol. 101(C).
    12. 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.
    13. Sudtachat, Kanchala & Mayorga, Maria E. & Mclay, Laura A., 2016. "A nested-compliance table policy for emergency medical service systems under relocation," Omega, Elsevier, vol. 58(C), pages 154-168.
    14. Yoon, Soovin & Albert, Laura A., 2020. "A dynamic ambulance routing model with multiple response," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    15. Wang, Wei & Wu, Shining & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2021. "Emergency facility location problems in logistics: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    16. Laura A. McLay & Maria E. Mayorga, 2013. "A Dispatching Model for Server-to-Customer Systems That Balances Efficiency and Equity," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 205-220, May.
    17. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    18. Amir Ardestani-Jaafari & Beste Kucukyazici, 2022. "Improving Patient Transfer Protocols for Regional Stroke Networks," Management Science, INFORMS, vol. 68(9), pages 6610-6633, September.
    19. Soovin Yoon & Laura A. Albert, 2018. "An expected coverage model with a cutoff priority queue," Health Care Management Science, Springer, vol. 21(4), pages 517-533, December.
    20. Jenkins, Phillip R. & Lunday, Brian J. & Robbins, Matthew J., 2020. "Robust, multi-objective optimization for the military medical evacuation location-allocation problem," Omega, Elsevier, vol. 97(C).

    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:spr:annopr:v:271:y:2018:i:2:d:10.1007_s10479-018-2760-z. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.springer.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.