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

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  • 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
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    References listed on IDEAS

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    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).

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