Abstract
This paper studies and compares various optimization approaches ranging from classical optimization to machine learning to respond swiftly and optimally in casualty incidents. Key points of interest in the comparison are the solution quality and the speed of finding it. In multiple-casualty scenarios knowing both is essential to choosing the correct method. A set of 960 synthetic MCI scenarios of different settings are being considered here to give an indication of scalability. For these scenarios, the aim is to optimize the number of victims receiving specialized treatments at the nearest available hospital.
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References
Carr, B.G., Caplan, J.M., Pryor, J.P., Branas, C.C.: A meta-analysis of prehospital care times for trauma. Prehospital Emerg. Care 10(2), 198–206 (2006)
Chu, X., Zhong, Q.Y., Khokhar, S.G.: Triage scheduling optimization for mass casualty and disaster response. APJOR 32(06), 1550041 (2015)
Ji, S., Zheng, Y., Wang, Z., Li, T.: A deep reinforcement learning-enabled dynamic redeployment system for mobile ambulances. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, issue number 1, pp. 1–20 (2019)
Wilson, D.T., Hawe, G.I., Coates, G., Crouch, R.S.: A multi-objective combinatorial model of casualty processing in major incident response. EJOR 230(3), 643–655 (2013)
Viana, M.S., Morandin Junior, O., Contreras, R.C.: A modified genetic algorithm with local search strategies and multi-crossover operator for job shop scheduling problem. Sensors 20(18), 5440 (2020)
Amjad, M.K., et al.: Recent research trends in genetic algorithm based flexible job shop scheduling problems. Math. Probl. Eng. 2018, 1–32 (2018). https://downloads.hindawi.com/journals/mpe/2018/9270802.pdf
Al Aqel, G., Li, X., Gao, L.: A modified iterated greedy algorithm for flexible job shop scheduling problem. CJME 32(1), 1–11 (2019). https://doi.org/10.1186/s10033-019-0337-7
Lunardi, W.T., Birgin, E.G., Laborie, P., Ronconi, D.P., Voos, H.: Mixed integer linear programming and constraint programming models for the online printing shop scheduling problem. Comput. Oper. Res. 123, 105020 (2020)
Nawaz, M., Enscore, E.E., Jr., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11(1), 91–95 (1983)
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This research was sponsored by the NATO Science for Peace and Security Programme under grant SPS MYP G5700.
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Nistor, M.S., Moll, M., Pham, T.S., Pickl, S.W., Budde, D. (2022). Resource Optimization in Mass Casualty Management: A Comparison of Methods. In: Trautmann, N., Gnägi, M. (eds) Operations Research Proceedings 2021. OR 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-08623-6_61
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DOI: https://doi.org/10.1007/978-3-031-08623-6_61
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