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.
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References
Alsalloum, O. I., & Rand, G. K. (2006). Extensions to emergency vehicle location models. Computers & Operations Research, 33(9), 2725–2743.
Bandara, D., Mayorga, M. E., & McLay, L. A. (2012). Optimal dispatching strategies for emergency vehicles to increase patient survivability. International Journal of Operational Research, 15(2), 195–214.
Bandara, D., Mayorga, M. E., & McLay, L. A. (2014). Priority dispatching strategies for EMS systems. Journal of the Operational Research Society, 65(4), 572–587.
Bastian, N. D. (2010). A robust, multi-criteria modeling approach for optimizing aeromedical evacuation asset emplacement. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 7(1), 5–23.
Bastian, N. D., Fulton, L. V., Mitchell, R., Pollard, W., Wierschem, D., & Wilson, R. (2012). The future of vertical lift: Initial insights for aircraft capability and medical planning. Military Medicine, 177(7), 863–869.
Bixby, R. E. (2012). A brief history of linear and mixed-integer programming computation. Documenta Mathematica, Extra Volume: Optimization Stories, 107–121.
Carter, G. M., Chaiken, J. M., & Ignall, E. (1972). Response areas for two emergency units. Operations Research, 20(3), 571–594.
Clarke, J. E., & Davis, P. R. (2012). Medical evacuation and triage of combat casualties in Helmand Province, Afghanistan: October 2010–April 2011. Military Medicine, 177(11), 1261–1266.
Cox, M. (2016). Bell touts future army helicopter design: ’V-280 is not a V-22’. http://www.military.com/daily-news/2016/01/15/bell-touts-future-army-helicopter-design-v280-is-not-a-v22.html. Accessed 7 September 2016.
De Lorenzo, R. A. (2003). Military casualty evacuation: MEDEVAC (pp. 45–59)., Aeromedical evacuation: Management of acute and stabilized patients New York: Springer.
Department of Defense. (2016). Defense Casualties Analysis System (DCAS) Operation FREEDOM’S SENTINEL (OFS). https://www.dmdc.osd.mil/dcas/pages/casualties_ofs.xhtml. Accessed 22 December 2016.
Department of the Army. (2000). Field manual 8-10-6, medical evacuation in a theater of operations.
Department of the Army. (2016). Army Techniques Publication 4-02.2, medical evacuation. Change 1.
Eastridge, B. J., Mabry, R. L., Seguin, P., Cantrell, J., Tops, T., Uribe, P., et al. (2012). Death on the battlefield (2001–2011): Implications for the future of combat casualty care. Journal of Trauma and Acute Care Surgery, 73(6), S431–S437.
Fish, P. N. (2014). Army medical officer’s guide. Mechanicsburg: Stackpole Books.
Fulton, L. V., Lasdon, L. S., McDaniel, R. R., & Coppola, M. N. (2010). Two-stage stochastic optimization for the allocation of medical assets in steady-state combat operations. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 7(2), 89–102.
Fulton, L., McMurry, P., & Kerr, B. (2009). A Monte Carlo simulation of air ambulance requirements during major combat operations. Military Medicine, 174(6), 610–614.
Garrett, M. X. (2013). USCENTCOM review of MEDEVAC procedures in Afghanistan. Technical report, United States Central Command.
Grannan, B. C., Bastian, N. D., & McLay, L. A. (2015). A maximum expected covering problem for locating and dispatching two classes of military medical evacuation air assets. Optimization Letters, 9(8), 1511–1531.
Green, L. V., & Kolesar, P. J. (2004). Anniversary article: Improving emergency responsiveness with management science. Management Science, 50(8), 1001–1014.
Gross, D., & Harris, C. M. (1998). Fundamentals of queueing theory (4th ed.). Hoboken: Wiley.
Hoffman, M. (2015). Army wants more adaptive HH-60 medical evacuation systems. http://www.military.com/daily-news/2015/04/03/army-wants-more-adaptive-hh60-medical-evacuation-systems.html. Accessed 7 September 2016.
International Council on Security and Development (ICOS). (2008). Afghanistan–Pakistan Insurgent activities in Afghanistan and Pakistan (2007). http://www.icosgroup.net/wp-content/gallery/taliban-presence/016_map.png. Accessed 4 January 2017.
Jarvis, J. P. (1985). Approximating the equilibrium behavior of multi-server loss systems. Management Science, 31(2), 235–239.
Keneally, S. K., Robbins, M. J., & Lunday, B. J. (2016). A Markov decision process model for the optimal dispatch of military medical evacuation assets. Health Care Management Science, 19(2), 111–129.
Kotwal, R. S., Howard, J. T., Orman, J. A., Tarpey, B. W., Bailey, J. A., Champion, H. R., et al. (2016). The effect of a golden hour policy on the morbidity and mortality of combat casualties. JAMA Surgery, 151(1), 15–24.
Kuisma, M., Holmström, P., Repo, J., Määttä, T., Nousila-Wiik, M., & Boyd, J. (2004). Prehospital mortality in an EMS system using medical priority dispatching: A community based cohort study. Resuscitation, 61(3), 297–302.
Kulkarni, V. G. (2009). Modeling and analysis of stochastic systems (2nd ed.). Boca Raton: CRC Press.
Lejeune, M. A., & Margot, F. (2016). Aeromedical battlefield evacuation under endogenous uncertainty. Technical report, Carnegie Mellon University, Pittsburg, PA.
Leoni, R. D. (2007). Black hawk: The story of a world class helicopter. Reston: American Institute of Aeronautics.
MacFarlane, C., & Benn, C. (2003). Evaluation of emergency medical services systems: A classification to assist in determination of indicators. Emergency Medicine Journal, 20(2), 188–191.
Malsby, R. F, I. I. I., Quesada, J., Powell-Dunford, N., Kinoshita, R., Kurtz, J., Gehlen, W., et al. (2013). Prehospital blood product transfusion by US Army MEDEVAC during combat operations in Afghanistan: A process improvement initiative. Military Medicine, 178(7), 785–791.
Maxwell, M. S., Restrepo, M., Henderson, S. G., & Topaloglu, H. (2010). Approximate dynamic programming for ambulance redeployment. INFORMS Journal on Computing, 22(2), 266–281.
McLay, L. A., & Mayorga, M. E. (2010). Evaluating emergency medical service performance measures. Health Care Management Science, 13(2), 124–136.
McLay, L. A., & Mayorga, M. E. (2013). A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities. IIE Transactions, 45(1), 1–24.
Nicholl, J., Coleman, P., Parry, G., Turner, J., & Dixon, S. (1999). Emergency priority dispatch systemsa new era in the provision of ambulance services in the UK. Pre-Hospital Immediate Care, 3(2), 71–75.
O’Shea, B. (2011). Saving lives on the battlefield. Military Medical/CBRN Technology, 15(6), 8–13.
Powell, W. B. (2011). Approximate dynamic programming: Solving the curses of dimensionality (2nd ed.). Princeton: Wiley.
Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. Hoboken: Wiley.
Rettke, A. J., Robbins, M. J., & Lunday, B. J. (2016). Approximate dynamic programming for the dispatch of military medical evacuation assets. European Journal of Operational Research, 254(3), 824–839.
Shenker, S., & Weinrib, A. (1989). The optimal control of heterogeneous queueing systems: A paradigm for load-sharing and routing. IEEE Transactions on Computers, 38(12), 1724–1735.
Stidham, S. (1985). Optimal control of admission to a queueing system. IEEE Transactions on Automatic Control, 30(8), 705–713.
Stidham, S, Jr. (2002). Analysis, design, and control of queueing systems. Operations Research, 50(1), 197–216.
Stidham, S, Jr., & Weber, R. (1993). A survey of Markov decision models for control of networks of queues. Queueing Systems, 13(1–3), 291–314.
Sundstrom, S. C., Blood, C. G., & Matheny, S. A. (1996). The optimal placement of casualty evacuation assets: A linear programming model. In Proceedings of the 28th conference on winter simulation (pp. 907–911). IEEE Computer Society.
White, M. (2016). Operation ENDURING FREEDOM (OEF), Fatalities by Provinces. http://icasualties.org/OEF/ByProvince.aspx. Accessed 22 December 2016.
Zeto, J., Yamada, W., & Collins, G. (2006). Optimizing the emplacement of scarce resources to maximize the expected coverage of a geographically variant demand function. In Proceedings of technical report, US Center for Army Analysis, Ft Belvoir.
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The views expressed in this paper are those of the authors and do not reflect the official policy or position of the United States Army, the United States Air Force, the Department of Defense, or the United States Government. The authors would like to thank the United States Army Medical Evacuation Proponency Directorate for its support of this research.
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Jenkins, P.R., Robbins, M.J. & Lunday, B.J. Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system. Ann Oper Res 271, 641–678 (2018). https://doi.org/10.1007/s10479-018-2760-z
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DOI: https://doi.org/10.1007/s10479-018-2760-z