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A maximum expected covering problem for locating and dispatching two classes of military medical evacuation air assets

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Abstract

Military medical evacuation (MEDEVAC) systems respond to casualty incidents and transport the most urgent casualties to a medical treatment facility via multiple types of air ambulance assets. Military MEDEVAC systems are subject to an uncertain number of service calls and each service call demands different system operations depending on type and the priority level. Therefore, military medical planners need an air MEDEVAC asset management system that determines how to dispatch multiple types of air assets to prioritized service calls to maintain a high likelihood of survival of the most urgent casualties. To reach this goal, we propose a novel binary linear programming (BLP) model to optimally locate two types of air assets and construct response districts using a dispatch preference list. Additionally, the BLP model balances the workload among assets and enforces contiguity in the first assigned locations for each air asset. The objective of the BLP model is to maximize the proportion of high-priority casualties responded to within a pre-specified time threshold while meeting performance benchmarks to other types of casualties. A spatial queuing approximation model is derived to provide inputs to the BLP model, which thus reflects the underlying queuing dynamics of the system. We illustrate the model and algorithms with a computational example that reflects realistic military data.

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References

  1. Ansari, S., McLay, L.A., Mayorga, M.E.: A Maximum Expected Covering Problem for Locating and Dispatching Servers. Under revision at Transportation Science, Virginia Commonwealth University (2014)

  2. Bastian, N.: A robust, multi-criteria modeling approach for optimizing aeromedical evacuation asset emplacement. J. Def. Model. Simul. 7(1), 5–23 (2010)

    Google Scholar 

  3. Bastian, N., Brown, D., Fulton, L., Mitchell, R., Pollard, W., Robinson, M., Wilson, R.: Analyzing the future of army aeromedical evacuation units and equipment: a mixed methods, requirements-based approach. Mil. Med. 178(3), 321–329 (2013)

    Article  Google Scholar 

  4. Bastian, N., Fulton, L.V., Mitchell, R., Pollard, W., Wierschem, D., Wilson, R.: The future of vertical lift: initial insights for aircraft capability and medical planning. Mil. Med. 177(7), 863–869 (2012)

    Article  Google Scholar 

  5. Bastian, N., Fulton, L.V.: Aeromedical evacuation planning using geospatial decision-support. Mil. Med. 179(2), 174–182 (2014)

    Article  Google Scholar 

  6. Batta, R., Dolan, J.M., Krishnamurthy, N.N.: The maximal expected covering location problem: revisited. Transp. Sci. 23(4), 277–287 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  7. Bouma, M.F.: Medical evacuation and treatment capabilities optimization model metcom. In: Proceedings of Technical Report, Naval Postgraduate School, California (2005)

  8. Budge, S., Ingolfsson, A., Erkut, E.: Approximating vehicle dispatch probabilities for emergency service systems with location-specific service times and multiple units per location. Op. Res. 57(1), 251–255 (2009)

    Article  MATH  Google Scholar 

  9. Chelst, K.R., Barlach, Z.: Multiple unit dispatches in emergency services: models to estimate system performance. Manag. Sci. 27(12), 1390–1409 (1981)

    Article  Google Scholar 

  10. Church, R., ReVelle, C.: The maximal covering location problem. Pap. Reg. Sci. Assoc. 32, 101–108 (1974)

    Article  Google Scholar 

  11. Daskin, M.S.: A maximum expected covering location model: formulation, properties and heuristic solution. Transp. Sci. 17(1), 48–70 (1983)

    Article  Google Scholar 

  12. Fulton, L., Lasdon, L., McDaniel, R., Coppola, M.: Two-state stochastic optimization for the allocation of medical assets in steady-state combat operations. J. Def. Model. Simul. 7(2), 89–102 (2010)

    Google Scholar 

  13. Fulton, L., McMurry, P., Kerr, B.: A monte carlo simulation of air ambulance requirements during major combat operations. Mil. Med. 174(6), 610–614 (2009)

    Article  Google Scholar 

  14. Jarvis, J.P.: Approximating the equilibrium behavior of multi-server loss systems. Manag. Sci. 31(2), 235–239 (1985)

    Article  MATH  Google Scholar 

  15. Larson, R.C.: A hypercube queuing model for facility location and redistricting in urban emergency services. Comput. Op. Res. 1(1), 67–95 (1974)

    Article  Google Scholar 

  16. Larson, R.C.: Approximating the performance of urban emergency service systems. Op. Res. 23(5), 845–868 (1975)

    Article  MATH  Google Scholar 

  17. Larson, R.C., McKnew, M.A.: Police patrol-initiated activities within a systems queuing model. Manag. Sci. 28(7), 759–774 (1982)

    Article  MATH  Google Scholar 

  18. Mandell, M.B.: Covering models for two-tiered emergency medical service systems. Locat. Sci. 6(1–4), 355–368 (1998)

    Article  Google Scholar 

  19. Marianov, V., ReVelle, C.: A probabilistic fire-protection siting model with joint vehicle reliability requirements. Pap. Reg. Sci. 71(3), 217–241 (1992)

    Article  Google Scholar 

  20. Marianov, V., Serra, D.: Hierarchical location-allocation models for congested systems. Eur. J. Op. Res. 135(1), 195–208 (2001)

    Article  MATH  Google Scholar 

  21. McLay, L.A.: A maximum expected covering location model with two types of servers. IIE Trans. 41(8), 730–741 (2009)

    Article  Google Scholar 

  22. Federation of American Scientists. Uh-60 black hawk, uh-60l black hawk, uh-60q medevac, mh-60g pave hawk, hh-60g pave hawk, ch-60 sea hawk. Accessed 13 Nov 2013

  23. ReVelle, C., Marianov, V.: A probabilistic FLEET model with individual vehicle reliability requirements. Eur. J. Op. Res. 53(1), 93–105 (1991)

    Article  MATH  Google Scholar 

  24. Zeto, J.F., Yamada, W., Collins, G.: 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 (2006)

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Acknowledgments

The first and third authors were supported by the U.S. Department of the Army under Grant Award Number W911NF-10-1-0176. The second author was supported by the National Science Foundation under Grant No. DGE1255832. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the United States Army, National Science Foundation, Virginia Military Institute, Pennsylvania State University, or University of Wisconsin-Madison.

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Correspondence to Benjamin C. Grannan.

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Grannan, B.C., Bastian, N.D. & McLay, L.A. A maximum expected covering problem for locating and dispatching two classes of military medical evacuation air assets. Optim Lett 9, 1511–1531 (2015). https://doi.org/10.1007/s11590-014-0819-6

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  • DOI: https://doi.org/10.1007/s11590-014-0819-6

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