A first approach to a hybrid algorithm for mobile emergency resources allocation
Redondo, Johan Manuel
Barrera, José Octaviano
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We present a hybrid algorithm based on Genetic Algorithms and Discrete Event Simulation that computes the algorithmic-optimal location of emergency resources. Parameters for the algorithm were obtained from computed historical statistics of the Bogotá Emergency Medical Services. Considerations taken into account are: (1) no more than a single resource is sent to an incident, (2) resources are selected according to incidentpriorities (3) distance from resource base to incident location is also considered for resource assignment and (4) all resources must be used equally. For every simulation, a different set of random incidents is generated so it’s possible to use the algorithm with an updated set of historical incidents. We found that the genetic algorithm converges so we can consider its solution as an optimal. With the algorithmic-optimal solution we found that arrival times are shorter than the historical ones. It’s also possible to compute the amount of required resources to reduce even more the arrival times. Since every Discrete Event Simulation takes a considerable amount of time the whole algorithm takes a heavy amount of time for large simulation time-periods and for many individuals for generation in the genetic algorithm, so an optimization approach is the next step in our research. Also, less restricted considerations must be taken into account for future developments in this topic.
/*ref*/Barrachina, J., Garrido, P., Fogue, M., Martinez, F. J., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2014). Reducing emergency services arrival time by using vehicular communications and Evolution Strategies. Expert Systems with Applications, 41(4), 1206–1217. https://doi.org/10.1016/J.ESWA.2013.08.004
/*ref*/Beck, A. (2008). Simulation: the practice of model development and use. Journal of Simulation, 2.
/*ref*/Benatar, S. R., & Ashcroft, R. (2017). International Perspectives on Resource Allocation. In S. R. Quah (Ed.), International Encyclopedia of Public Health (Second Edition) (Second Edi, pp. 316–321). Oxford: Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-803678-5.00380-5
/*ref*/Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety Science, 35(1–3), 41–57. https://doi.org/10.1016/S0925-7535(00)00021-7
/*ref*/Fogel, D. B., Kennedy, J., Eberhart, R. C., Shi, Y., Jacob, C., Peter, E., … Francone, F. D. (n.d.). Genetic Programming: An Introduction.
/*ref*/Hawe, G. I., Coates, G., Wilson, D. T., & Crouch, R. S. (2015). Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident. Engineering Applications of Artificial Intelligence, 46, 336–345. https://doi.org/https://doi.org/10.1016/j.engappai.2015.06.023
/*ref*/Huang, Y., & Fan, Y. (2011). Modeling Uncertainties in Emergency Service Resource Allocation. Journal of Infrastructure Systems, 17(1), 35–41. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000040
/*ref*/Kim, T. H., Lee, K., Shin, S. Do, Ro, Y. S., Tanaka, H., Yap, S., … Leong, B. (2017). Association of the Emergency Medical Services–Related Time Interval with Survival Outcomes of Out-of-Hospital Cardiac Arrest Cases in Four Asian Metropolitan Cities Using the Scoop-and-Run Emergency Medical Services Model. The Journal of Emergency Medicine, 53(5), 688–696.e1. https://doi.org/10.1016/J.JEMERMED.2017.08.076
/*ref*/Luscombe, R., & Kozan, E. (2016). Dynamic resource allocation to improve emergency department efficiency in real time. European Journal of Operational Research, 255(2), 593–603. https://doi.org/https://doi.org/10.1016/j.ejor.2016.05.039
/*ref*/Pradhananga, R., Mutlu, F., Pokharel, S., Holguín-Veras, J., & Seth, D. (2016). An integrated resource allocation and distribution model for pre-disaster planning. Computers & Industrial Engineering, 91, 229–238. https://doi.org/10.1016/j.cie.2015.11.010
/*ref*/Schluck, G., Wu, W., Whyte, J., & Abbott, L. (2018). Emergency department arrival times in Florida heart failure patients utilizing Fisher-Rao curve registration: A descriptive population-based study. Heart & Lung. https://doi.org/10.1016/j.hrtlng.2018.05.020
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