References
- Baker, J. R., Clayton, E. R., & Taylor, B. W. III, (1989). A non-linear multi-criteria programming approach for determining county emergency medical service ambulance allocations. The Journal of the Operational Research Society, 40(5), 423–432. https://doi.org/10.1057/jors.1989.69
- 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. https://doi.org/10.1504/IJOR.2012.048867
- 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. https://doi.org/10.1057/jors.2013.95
- Bastian, N. (2010). A robust, multi-criteria modeling approach for optimizing aeromedical evacuation asset emplacement. The Journal of Defense Modeling and Simulation, 7(1), 5–23. https://doi.org/10.1177/1548512909354615
- Buckenmaier, C., & Mahoney, P. F. (2015). Combat anesthesia: The first 24 hours. Office of the Surgeon General, United States Army.
- Department of the Army, U. S. (2015). Atp 4-02.55 army health system support planning. Department of the Army (US).
- Department of the Army, U. S. (2019). Fm 4-02.2 medical evacuation. Department of the Army (US).
- DuBois, E., & Albert, L. A. (2022). Dispatching policies during prolonged mass casualty incidents. Journal of the Operational Research Society, 73(11), 2536–2550. https://doi.org/10.1080/01605682.2021.1999181
- Egesdal, M., Fathauer, C., Louie, K., Neuman, J., Mohler, G., & Lewis, E. (2010). Statistical and stochastic modeling of gang rivalries in los angeles. SIAM Undergraduate Research Online, 3, 72–94. https://doi.org/10.1137/09S010459
- Erkut, E., Ingolfsson, A., & Erdoğan, G. (2008). Ambulance location for maximum survival. Naval Research Logistics (NRL), 55(1), 42–58. https://doi.org/10.1002/nav.20267
- 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. https://doi.org/10.1007/s11590-014-0819-6
- Graves, E. S., Jenkins, P. R., Robbins, M. J. (2021). Analyzing the impact of triage classification errors on military medical evacuation dispatching policies. Proceedings of the 2021 Winter Simulation Conference.
- Green, L., & Kolesar, P. (2004). Improving emergency responsiveness with management science. Management Science, 50(8), 1001–1014. https://doi.org/10.1287/mnsc.1040.0253
- Hawkes, A. G. (1971). Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1), 83–90. https://doi.org/10.1093/biomet/58.1.83
- Jenkins, P. R., Lunday, B. J., & Robbins, M. J. (2020). Robust, multi-objective optimization for the military medical evacuation location-allocation problem. Omega, 97, 102088. https://doi.org/10.1016/j.omega.2019.07.004
- Jenkins, P. R., Robbins, M. J., & Lunday, B. J. (2018). Examining military medical evacuation dispatching policies utilizing a markov decision process model of a controlled queueing system. Annals of Operations Research, 271(2), 641–678. https://doi.org/10.1007/s10479-018-2760-z
- Jenkins, P. R., Robbins, M. J., & Lunday, B. J. (2021a). Approximate dynamic programming for military medical evacuation dispatching policies. INFORMS Journal on Computing, 33(1), 2–26. https://doi.org/10.1287/ijoc.2019.0930
- Jenkins, P. R., Robbins, M. J., & Lunday, B. J. (2021b). Approximate dynamic programming for the military aeromedical evacuation dispatching, preemption-rerouting, and redeployment problem. European Journal of Operational Research, 290(1), 132–143. https://doi.org/10.1016/j.ejor.2020.08.004
- 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. https://doi.org/10.1007/s10729-014-9297-8
- Kotwal, R. S., Howard, J. T., Orman, J. A., Tarpey, B. W., Bailey, J. A., Champion, H. R., Mabry, R. L., Holcomb, J. B., & Gross, K. R. (2016). The Effect of a Golden Hour Policy on the Morbidity and Mortality of Combat Casualties. JAMA Surgery, 151(1), 15–24. https://doi.org/10.1001/jamasurg.2015.3104
- Kroese, D., & Botev, Z. (2013). Spatial process generation. Analysis, Modeling and Simulation of Complex Structures, 382–384.
- Kulkarni, V. G. (2016). Modeling and analysis of stochastic systems. Crc Press.
- Laub, P. J., Lee, Y., & Taimre, T. (2021). The elements of hawkes processes. Springer.
- Lee, H.-R., & Lee, T. (2018). Markov decision process model for patient admission decision at an emergency department under a surge demand. Flexible Services and Manufacturing Journal, 30(1–2), 98–122. https://doi.org/10.1007/s10696-017-9276-8
- Lewis, E., Mohler, G., Brantingham, P., & Bertozzi, A. (2012). Self-exciting point process models of civilian deaths in Iraq. Security Journal, 25(3), 244–264. https://doi.org/10.1057/sj.2011.21
- Li, M., Carter, A., Goldstein, J., Hawco, T., Jensen, J., & Vanberkel, P. (2021). Determining ambulance destinations when facing offload delays using a markov decision process. Omega, 101, 102251. https://doi.org/10.1016/j.omega.2020.102251
- Mayorga, M. E., Bandara, D., & McLay, L. A. (2013). Districting and dispatching policies for emergency medical service systems to improve patient survival. IIE Transactions on Healthcare Systems Engineering, 3(1), 39–56. https://doi.org/10.1080/19488300.2012.762437
- McLay, L. A. (2011). Emergency medical service systems that improve patient survivability. In Wiley encyclopedia of operations research and management science. American Cancer Society.
- McLay, L. A., & Mayorga, M. E. (2010). Evaluating emergency medical service performance measures. Health Care Management Science, 13(2), 124–136. https://doi.org/10.1007/s10729-009-9115-x
- 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. https://doi.org/10.1080/0740817X.2012.665200
- Phillips, R. C. (2005). Bosnia-herzegovina: The U.S. armys role in peace enforcement operations 1995-2004. Center of Military History.
- Pinto, L., Silva, P., & Young, T. (2015). A generic method to develop simulation models for ambulance systems. Simulation Modelling Practice and Theory, 51, 170–183. https://doi.org/10.1016/j.simpat.2014.12.001
- Rettke, A., Robbins, M., & Lunday, B. (2016). Approximate dynamic programming for the dispatch of military medical evacuation assets. European Journal of Operational Research, 254(3), 824–839. https://doi.org/10.1016/j.ejor.2016.04.017
- Robbins, M. J., Jenkins, P. R., Bastian, N. D., & Lunday, B. J. (2020). Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation. Omega, 91, 102020. https://doi.org/10.1016/j.omega.2018.12.009
- Rodriguez, C. A., Jenkins, P. R., & Robbins, M. J. (2023). Solving the joint military medical evacuation problem via a random forest approximate dynamic programming approach. Expert Systems with Applications, 221, 119751. https://doi.org/10.1016/j.eswa.2023.119751
- Wu, X., Xu, R., Li, J., & Khasawneh, M. T. (2019). A simulation study of bed allocation to reduce blocking probability in emergency departments: A case study in china. Journal of the Operational Research Society, 70(8), 1376–1390. https://doi.org/10.1080/01605682.2018.1506430
- Yue, Y., Marla, L., Krishnan, R., & Heinz, H. (2012). An efficient simulation-based approach to ambulance fleet allocation and dynamic redeployment. Proceedings of the National Conference on Artificial Intelligence, 26(1), 398–405.
- Zeto, J., Yamada, W., & Collins, G. (2006). Optimizing the emplacement of scarce resources to maximize the expected coverage of a geographically variant demand function. Proceedings of Technical Report, U.S. Center for Army Analysis, Ft. Belvoir.