Modelos matemáticos para la evacuación de personas en la cadena de suministro humanitaria: una revisión

Palabras clave: cadena de suministro humanitaria, logistica humaniaria, modelos matematicos, optimizacion, desastres naturales

Resumen

En la actualidad los desastres naturales representan una amenaza a nivel mundial, ya que se relacionan con el cambio climático. Debido a la naturaleza destructiva de estos fenómenos naturales, el impacto económico y social que deja en las poblaciones y países es considerablemente alto. La cadena de suministro humanitaria está logrando gran interés tanto en ámbito académico, empresarial y gubernamental debido a su relevancia para hacer frente a los desastres naturales. En el presente trabajo, se realizó una revisión de la literatura de modelos matemáticos de evacuación en la cadena de suministro humanitaria. El 36% de los artículos se orientan a la fase predesastre, 32% a posdesastre y 32% en una fase integrada. Asimismo, el 83% de los artículos plantean modelos determinísticos y el 17% no determinísticos. Los métodos más comunes para resolver los modelos de optimización son algoritmos metaheurísticos, flujo de redes y problema de enrutamiento de vehículos. Por su parte, los métodos planteados para resolver los modelos sin optimización son programación estocástica, modelos probabilísticos, procesos de Markov y modelos basados en agentes.  Como trabajos futuros podría sugerirse abordar problemas enfocados a la etapa de predesastre con mútiples periodos de tiempo para poder establecer estrategias adecuadas con preparativos más completos.

Descargas

La descarga de datos todavía no está disponible.

Citas

Behl, A., & Dutta, P. (2018). Humanitarian supply chain management: a thematic literature review and future directions of research. Annals of Operations Research, 283, 1001–1044. https://doi.org/10.1007/s10479-018-2806-2

Capacci, A., & Mangano, S. (2015). Las catástrofes naturales. Cuaderrnos de Geografía: Revista Colombiana de Geografía, 24(2), 35–51. Disponible en: https://www.redalyc.org/articulo.oa?id=281839793003

Chiappetta, C.J.; Sobreiro, V.A.; de Sousa Jabbour, A.B.L.; Campos, L.M.S.; Mariano, E.B.; Renwick, D.W.S. (2019). An analysis of the literature on humanitarian logistics and supply chain management: Paving the way for future studies. Applications of OR in Disaster Relief Operations, 283, 289–307. https://doi.org/10.1007/s10479-017-2536-x

Hezam, I. M., & Nayeem, M. K. (2020). A Systematic Literature Review on Mathematical Models of Humanitarian Logistics. Symmetry, MDPI, 13(1), 11. https://doi.org/10.3390/sym13010011

Hu, H., He, J., He, X., Yang, W., Nie, J., & Ran, B. (2019). Emergency material scheduling optimization model and algorithms: A review. Journal of traffic and transportation engineering, 5, 441-454. https://doi.org/10.1016/j.jtte.2019.07.001

Jaganmohan, M. (2022). Annual number of natural disaster events globally from 2007 to 2021. Recuperado de https://www.statista.com/statistics/510959/number-of-natural-disasters-events-globally/

Kimms, A., & Maiwald, M. (2017). An exact network flow formulation for cell-based evacuation in urban areas. Naval Res Logistics, 64, 547–555. https://doi.org/10.1002/nav.21772

Madani S., H., Arshadi K., A., & Tavakkoli-Moghaddam, R. (2021). Solving a new bi-objective model for relief logistics in a humanitarian supply chain by bi-objective meta-heuristic algorithms. Scientia Iranica, 28, 2948–2971. Doi: 10.24200/SCI.2020.53823.3438

Melendez, B., Machiani, S. G., & Atsushi, N. (2021). Modelling traffic during Lilac Wildfire evacuation using cellular data. Transportation Research Interdisciplinary Perspectives, 9(100335). https://doi.org/10.1016/j.trip.2021.100335

Mollah, A. K., Sadhukhan, S., Das, P., & Anis, M. Z. (2018). A cost optimization model and solutions for shelter allocation and relief distribution in flood scenario. International Journal of Disaster Risk Reduction, 31, 1187–1198. https://doi.org/10.1016/j.ijdrr.2017.11.018

Molina, J., López, A., Hernández, A., Martínez, I. (2017). A Multi-start Algorithm with Intelligent Neighborhood Selection for solving multi-objective humanitarian vehicle routing problems. 1-23. https://doi.org/10.1007/s10732-017-9360-y

Nayeri, S., Tavakkoli-Moghaddam, R., Sazvar, Z., & Heydari, J. (2020). Solving an Emergency Resource Planning Problem with Deprivation Time by a Hybrid MetaHeuristic Algorithm. Journal of Quality Engineering and Production Optimization, 5(1), 65–86. Doi: 10.22070/JQEPO.2020.5379.1150

Panwar, V., & Sen, S. (2019). Economic Impact of Natural Disasters: An Empirical Re-examination. Margin: The Journal of Applied Economic Research, 13(1), 109–139. https://doi.org/10.1177/0973801018800087

Referencias

Behl, A., & Dutta, P. (2018). Humanitarian supply chain management: a thematic literature review and future directions of research. Annals of Operations Research, 283, 1001–1044. https://doi.org/10.1007/s10479-018-2806-2

Capacci, A., & Mangano, S. (2015). Las catástrofes naturales. Cuaderrnos de Geografía: Revista Colombiana de Geografía, 24(2), 35–51. Disponible en: https://www.redalyc.org/articulo.oa?id=281839793003

Chiappetta, C.J.; Sobreiro, V.A.; de Sousa Jabbour, A.B.L.; Campos, L.M.S.; Mariano, E.B.; Renwick, D.W.S. (2019). An analysis of the literature on humanitarian logistics and supply chain management: Paving the way for future studies. Applications of OR in Disaster Relief Operations, 283, 289–307. https://doi.org/10.1007/s10479-017-2536-x

Hezam, I. M., & Nayeem, M. K. (2020). A Systematic Literature Review on Mathematical Models of Humanitarian Logistics. Symmetry, MDPI, 13(1), 11. https://doi.org/10.3390/sym13010011

Hu, H., He, J., He, X., Yang, W., Nie, J., & Ran, B. (2019). Emergency material scheduling optimization model and algorithms: A review. Journal of traffic and transportation engineering, 5, 441-454. https://doi.org/10.1016/j.jtte.2019.07.001

Jaganmohan, M. (2022). Annual number of natural disaster events globally from 2007 to 2021. Recuperado de https://www.statista.com/statistics/510959/number-of-natural-disasters-events-globally/

Kimms, A., & Maiwald, M. (2017). An exact network flow formulation for cell-based evacuation in urban areas. Naval Res Logistics, 64, 547–555. https://doi.org/10.1002/nav.21772

Madani S., H., Arshadi K., A., & Tavakkoli-Moghaddam, R. (2021). Solving a new bi-objective model for relief logistics in a humanitarian supply chain by bi-objective meta-heuristic algorithms. Scientia Iranica, 28, 2948–2971. Doi: 10.24200/SCI.2020.53823.3438

Melendez, B., Machiani, S. G., & Atsushi, N. (2021). Modelling traffic during Lilac Wildfire evacuation using cellular data. Transportation Research Interdisciplinary Perspectives, 9(100335). https://doi.org/10.1016/j.trip.2021.100335

Mollah, A. K., Sadhukhan, S., Das, P., & Anis, M. Z. (2018). A cost optimization model and solutions for shelter allocation and relief distribution in flood scenario. International Journal of Disaster Risk Reduction, 31, 1187–1198. https://doi.org/10.1016/j.ijdrr.2017.11.018

Molina, J., López, A., Hernández, A., Martínez, I. (2017). A Multi-start Algorithm with Intelligent Neighborhood Selection for solving multi-objective humanitarian vehicle routing problems. 1-23. https://doi.org/10.1007/s10732-017-9360-y

Nayeri, S., Tavakkoli-Moghaddam, R., Sazvar, Z., & Heydari, J. (2020). Solving an Emergency Resource Planning Problem with Deprivation Time by a Hybrid MetaHeuristic Algorithm. Journal of Quality Engineering and Production Optimization, 5(1), 65–86. Doi: 10.22070/JQEPO.2020.5379.1150

Panwar, V., & Sen, S. (2019). Economic Impact of Natural Disasters: An Empirical Re-examination. Margin: The Journal of Applied Economic Research, 13(1), 109–139. https://doi.org/10.1177/0973801018800087

Rambha, T., Nozick, L. K., & Davidson, R. (2021). Modeling hurricane evacuation behavior using a dynamic discrete choice framework. Transportation Research Part B: Methodological, 150, 75–100. https://doi.org/10.1016/j.trb.2021.06.003

Santana-Robles., F., Hernández-Gress, E. S., Hernández-Gress, N., & Granillo-Macias., R. (2021). Metaheuristics in the Humanitarian Supply Chain. Algorithms, 14(12), 364. https://doi.org/10.3390/a14120364

Sopha, B. M., Achsan, R. E. D., & Asih, A. M. S. (2019). Mount Merapi eruption: Simulating dynamic evacuation and volunteer coordination using agent-based modeling approach. Journal of Humanitarian Logistics and Supply Chain Management, 9(2), 292–322. https://doi.org/10.1108/JHLSCM-05-2018-0035

Szmigiera, M. (2022). Countries with the most natural disasters in 2021. Recuperado de https://www.statista.com/statistics/269652/countries-with-the-most-natural-disasters/

Taneja, L., & Bolia, N. B. (2018). Pedestrian control measures for efficient emergency response management in mass gatherings. International Journal of Disaster Resilience in the Built Environment, 9(3), 273–290. https://doi.org/10.1108/IJDRBE-07-2017-0045

Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003, 14, 207–222. https://doi.org/10.1111/1467-8551.00375

Urata, J., & Pel, A. J. (2018). People’s Risk Recognition Preceding Evacuation and Its Role in Demand Modeling and Planning. Risk analysis, 38(5), 889–905. https://doi.org/10.1111/risa.12931

Wild, A. J., Bebbington, M. S., Lindsay, J. M., & Charlton, D. H. (2021). Modelling spatial population exposure and evacuation clearance time for the Auckland Volcanic Field, New Zealand. Journal of Volcanology and Geothermal Research, 416(107282). https://doi.org/10.1016/j.jvolgeores.2021.107282

Zhang, L., Cui, N. (2021). Humanitarian logistics and emergency relief management: hot perspectives and its optimization approach, 5th International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2021), Vol. 245. https://doi.org/10.1051/e3sconf/202124503036

Zeng, M. H., Wang, M., Chen, Y., & Yang, Z. (2021). Dynamic evacuation optimization model based on conflict-eliminating cell transmission and split delivery vehicle routing. Safety Science, 137(105166). https://doi.org/10.1016/j.ssci.2021.105166

Zhang, D., Huang, G., Ji, C., Liu, H., & Tang, Y. (2021). Pedestrian evacuation modeling and simulation in multi-exit scenarios. Physica A: Statistical Mechanics and its Applications, 582(126272). https://doi.org/10.1016/j.physa.2021.126272

Zhu, L., Gong, Y., Xu, Y., & Gu, Y. (2019). Emergency relief routing models for injured victims considering equity and priority. Annals of Operations Research, 283, 1573–1606. https://doi.org/10.1007/s10479-018-3089-3

Publicado
2023-01-05
Cómo citar
Santana-Robles, F., Granillo-Macias, R., Armas-Alvarez, B., & Beltrán Rodríguez, Z. (2023). Modelos matemáticos para la evacuación de personas en la cadena de suministro humanitaria: una revisión. Ingenio Y Conciencia Boletín Científico De La Escuela Superior Ciudad Sahagún, 10(19), 48-60. https://doi.org/10.29057/escs.v10i19.9902

Artículos más leídos del mismo autor/a

1 2 > >>