Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad
ilustraciones, diagramas
- Autores:
-
Rojas Trejos, Carlos Alberto
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/85888
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Logística humanitaria
Distribución de ayuda humanitaria
Restauración del acceso
Desastre natural súbito
Modelo matemático
Humanitarian logistics
Humanitarian aid distribution
Access restoration
Sudden natural disaster
Mathematical model
Asistencia por desastre
Infraestructura de transportes
Amenaza natural
Disaster relief
Transport infrastructure
Natural hazards
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad |
| dc.title.translated.eng.fl_str_mv |
Coordination of the distribution of humanitarian aid with the restoration of transitory road disruptions in areas affected by sudden natural disasters with constraints of accessibility |
| title |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad |
| spellingShingle |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Logística humanitaria Distribución de ayuda humanitaria Restauración del acceso Desastre natural súbito Modelo matemático Humanitarian logistics Humanitarian aid distribution Access restoration Sudden natural disaster Mathematical model Asistencia por desastre Infraestructura de transportes Amenaza natural Disaster relief Transport infrastructure Natural hazards |
| title_short |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad |
| title_full |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad |
| title_fullStr |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad |
| title_full_unstemmed |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad |
| title_sort |
Coordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidad |
| dc.creator.fl_str_mv |
Rojas Trejos, Carlos Alberto |
| dc.contributor.advisor.spa.fl_str_mv |
Meisel Donoso, José David Adarme Jaimes, Wilson |
| dc.contributor.author.spa.fl_str_mv |
Rojas Trejos, Carlos Alberto |
| dc.contributor.researchgroup.spa.fl_str_mv |
Sociedad, Economía y Productividad - SEPRO |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Logística humanitaria Distribución de ayuda humanitaria Restauración del acceso Desastre natural súbito Modelo matemático Humanitarian logistics Humanitarian aid distribution Access restoration Sudden natural disaster Mathematical model Asistencia por desastre Infraestructura de transportes Amenaza natural Disaster relief Transport infrastructure Natural hazards |
| dc.subject.proposal.spa.fl_str_mv |
Logística humanitaria Distribución de ayuda humanitaria Restauración del acceso Desastre natural súbito Modelo matemático |
| dc.subject.proposal.eng.fl_str_mv |
Humanitarian logistics Humanitarian aid distribution Access restoration Sudden natural disaster Mathematical model |
| dc.subject.unesco.spa.fl_str_mv |
Asistencia por desastre Infraestructura de transportes Amenaza natural |
| dc.subject.unesco.eng.fl_str_mv |
Disaster relief Transport infrastructure Natural hazards |
| description |
ilustraciones, diagramas |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-04-09T19:30:35Z |
| dc.date.available.none.fl_str_mv |
2024-04-09T19:30:35Z |
| dc.date.issued.none.fl_str_mv |
2024-03-01 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
| format |
http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/85888 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/85888 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
| dc.relation.references.spa.fl_str_mv |
Agarwal, S., Kant, R., & Shankar, R. (2019). Humanitarian supply chain management frameworks: A critical literature review and framework for future development. Benchmarking, 26(6), 1749–1780. https://doi.org/10.1108/BIJ-08-2018-0245 Ahmadi, M., Seifi, A., & Tootooni, B. (2015a). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, 75, 145–163. https://doi.org/10.1016/j.tre.2015.01.008 Akbari, V., & Salman, F. S. (2017a). Multi-vehicle prize collecting arc routing for connectivity problem. Computers and Operations Research, 82, 52–68. https://doi.org/10.1016/j.cor.2017.01.007 Akbari, V., & Salman, F. S. (2017b). Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity. European Journal of Operational Research, 257(2), 625–640. https://doi.org/https://doi.org/10.1016/j.ejor.2016.07.043 Akbari, V., Hesam, M., Kiam, R. (2021). A decomposition-based heuristic for a multicrew coordinated road restoration problem.Transportation Research Part D: Transport and Environment, 95, 1-35. https://doi.org/10.1016/j.trd.2021.102854 Akbari, V., Hesam, Sayarshad, H. (2022). Integrated and coordinated relief logistics and road recovery planning problem. Transportation Research Part D: Transport and Environment, 111, 1-50. https://doi.org/10.1016/j.trd.2022.103433 Akhtar, P., Marr, N. E., & Garnevska, E. V. (2012a). Coordination in humanitarian relief chains: chain coordinators. Journal of Humanitarian Logistics and Supply Chain Management, 2(1), 85–103. https://doi.org/10.1108/20426741211226019 Akin, D., & Kara, D. (2020). Multicriteria analysis of planned intercity bus terminals in the metropolitan city of Istanbul, Turkey. Transportation Research Part A: Policy and Practice, 132, 465–489. https://doi.org/https://doi.org/10.1016/j.tra.2019.12.003 Aksu, D. T., & Ozdamar, L. (2014a). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E-Logistics and Transportation Review, 61, 56–67. https://doi.org/10.1016/j.tre.2013.10.009 Al Theeb, N., & Murray, C. (2017). Vehicle routing and resource distribution in postdisaster humanitarian relief operations. International Transactions in Operational Research, 24(6), 1253–1284. https://doi.org/10.1111/itor.12308 Aldunate, R. G., Pena-Mora, F., & Robinson, G. E. (2005). Collaborative distributed decision making for large scale disaster relief operations: Drawing analogies from robust natural systems. Complexity, 11(2), 28–38. https://doi.org/10.1002/cplx.20106 Alinaghian, M., Aghaie, M., & Sabbagh, M. S. (2019). A mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles. Computers and Industrial Engineering, 131(17), 227–241. https://doi.org/10.1016/j.cie.2019.03.002 Altay, N., Prasad, S., & Tata, J. (2013). A dynamic model for costing disaster mitigation policies. Disasters, 37(3), 357–373. https://doi.org/10.1111/disa.12004 Alumur, S. A., Kara, B. Y., & Karasan, O. E. (2012). Multimodal hub location and hub network design. Omega, 40(6), 927–939. https://doi.org/10.1016/j.omega.2012.02.005 Anaya-Arenas, A. M., Renaud, J., & Ruiz, A. (2014a). Relief distribution networks: a systematic review. Annals of Operations Research, 223(1), 53–79. https://doi.org/10.1007/s10479-014-1581-y Anjomshoae, A., Hassan, A., & Wong, K. Y. (2019). An integrated AHP-based scheme for performance measurement in humanitarian supply chains. International Journal of Productivity and Performance Management, 68(5), 938–957. https://doi.org/10.1108/IJPPM-04-2018-0132 Aranda Pinilla, J. A., & Orjuela Castro, J. A. (2015). Optimización multiobjetivo en la gestión de cadenas de suministro de biocombustibles. Una revisión de la literatura. Ingeniería, 20(1). https://doi.org/10.14483/udistrital.jour.reving.2015.1.a03 Arif, A., Wang, Z., Chen, C., & Chen, B. (2020a). A Stochastic Multi-Commodity Logistic Model for Disaster Preparation in Distribution Systems. IEEE Transactions on Smart Grid, 11(1), 565–576. https://doi.org/10.1109/TSG.2019.2925620 Aros, S. K., & Gibbons, D. E. (2018). Exploring communication media options in an inter-organizational disaster response coordination network using agent-based simulation. European Journal of Operational Research, 269(2), 451–465. https://doi.org/10.1016/j.ejor.2018.02.013 Aslan, E., & Celik, M. (2019). Pre-positioning of relief items under road/facility vulnerability with concurrent restoration and relief transportation. Iise Transactions, 51(8), 847–868. https://doi.org/10.1080/24725854.2018.1540900 Averbakh, I. (2012). Emergency path restoration problems. Discrete Optimization, 9(1), 58–64. https://doi.org/10.1016/j.disopt.2012.01.001 Baharmand, H., Comes, T., & Lauras, M. (2020). Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters: A case study of the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction, 45(December 2019), 101455. https://doi.org/10.1016/j.ijdrr.2019.101455 Bai, X., Gao, J., & Liu, Y. (2018). Prepositioning emergency supplies under uncertainty. parametric optimization method. Engineering Optimization, 50(7), 1114–1133. https://doi.org/10.1080/0305215X.2017.1328508 Balcik, B. (2016). Special issue in humanitarian operations. Surveys in Operations Research and Management Science, 21(2), 29–30. https://doi.org/10.1016/j.sorms.2017.02.001 Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics Research and Applications, 11(2), 101–121. https://doi.org/10.1080/13675560701561789 Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22–34. https://doi.org/10.1016/j.ijpe.2009.09.008 Balcik, B., Silvestri, S., Rancourt, M. E., & Laporte, G. (2019). Collaborative Prepositioning Network Design for Regional Disaster Response. Production and Operations Management, 28(10), 2431–2455. https://doi.org/10.1111/poms.13053 Banomyong, R., & Sopadang, A. (2010). Using Monte Carlo simulation to refine emergency logistics response models: a case study. International Journal of Physical Distribution & Logistics Management, 40(8–9), 709–721. https://doi.org/10.1108/09600031011079346 Barrera, D., Velasco, N., & Amaya, C. A. (2012). A network-based approach to the multi-activity combined timetabling and crew scheduling problem: Workforce scheduling for public health policy implementation. Computers and Industrial Engineering, 63(4), 802–812. https://doi.org/10.1016/j.cie.2012.05.002 Baxter, A. E., Wilborn Lagerman, H. E., & Keskinocak, P. (2020). Quantitative modeling in disaster management: A literature review. IBM Journal of Research and Development, 64(1–2). https://doi.org/10.1147/JRD.2019.2960356 Baykasoğlu, A., Subulan, K., Taşan, A. S., & Dudaklı, N. (2019). A review of fleet planning problems in single and multimodal transportation systems. Transportmetrica A: Transport Science 15 (2), 631-697. Bealt, J., Fernández Barrera, J. C., & Mansouri, S. A. (2016). Collaborative relationships between logistics service providers and humanitarian organizations during disaster relief operations. Journal of Humanitarian Logistics and Supply Chain Management, 6(2), 118–144. https://doi.org/10.1108/JHLSCM-02-2015-0008 Berariu, R., Fikar, C., Gronalt, M., & Hirsch, P. (2015). Understanding the impact of cascade effects of natural disasters on disaster relief operations. International Journal of Disaster Risk Reduction, 12, 350–356. https://doi.org/https://doi.org/10.1016/j.ijdrr.2015.03.005 Berumen, S. a, & Llamazares, F. (2007). La utilidad de los métodos de decisión multicriterio (como el AHP) en un entorno de competitividad creciente. Cuadernos de Administración, 20(34), 65–87. Bhamidipati, S. (2015). Simulation Framework for Asset Management in Climate-change Adaptation of Transportation Infrastructure. Transportation Research Procedia, 8, 17–28. https://doi.org/https://doi.org/10.1016/j.trpro.2015.06.038 Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485–498. https://doi.org/10.1016/j.ijdrr.2017.01.017 Botti, L., Bragatto, P. A., Duraccio, V., Gnoni, M. G., & Mora, C. (2016). Adopting IOT technologies to control risks in confined space: A multi-criteria decision tool. Chemical Engineering Transactions, 53, 127–132. https://doi.org/10.3303/CET1653022 Braekers, K., Caris, A., & Janssens, G. K. (2014). Bi-objective optimization of drayage operations in the service area of intermodal terminals. Transportation Research Part E: Logistics and Transportation Review, 65, 50–69. https://doi.org/10.1016/j.tre.2013.12.012 Braekers, K., Hartl, R. F., Parragh, S. N., & Tricoire, F. (2016). A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience. European Journal of Operational Research, 248(2), 428–443. https://doi.org/10.1016/j.ejor.2015.07.028 Brower, R. S., Choi, S. O., Jeong, H. S., & Dilling, J. (2009). Forms of Inter-Organizational Learning in Emergency Management Networks. Journal of Homeland Security and Emergency Management, 6(1). %3CGo Cailloux, O., Mayag, B., Meyer, P., & Mousseau, V. (2013). Operational tools to build a multicriteria territorial risk scale with multiple stakeholders. Reliability Engineering & System Safety, 120, 88–97. https://doi.org/10.1016/j.ress.2013.06.004 Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4-13. Çelik, M. (2016). Network restoration and recovery in humanitarian operations: Framework, literature review, and research directions. Surveys in Operations Research and Management Science, 21(2), 47–61. https://doi.org/10.1016/j.sorms.2016.12.001 Çelik, M., Ergun, Ö., & Keskinocak, P. (2015). The post-disaster debris clearance problem under incomplete information. Operations Research, 63(1), 65–85. https://doi.org/10.1287/opre.2014.1342 Centre for Research on the Epidemiology of Disasters (CRED). (2020). EM-DAT: The Emergency Events. Chang, F.-S., Wu, J.-S., Lee, C.-N., & Shen, H.-C. (2014). Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling. Expert Systems with Applications, 41(6), 2947–2956. https://doi.org/10.1016/j.eswa.2013.10.026 Chen, X., Kong, Y., Dang, L., Hou, Y., & Ye, X. (2016). Exact and metaheuristic approaches for a bi-objective school bus scheduling problem. PLoS ONE, 11(4), 1–20. https://doi.org/10.1371/journal.pone.0132600 Chen, Y. xin, Tadikamalla, P. R., Shang, J., & Song, Y. (2020). Supply allocation: bi-level programming and differential evolution algorithm for Natural Disaster Relief. Cluster Computing, 23(1), 203–217. https://doi.org/10.1007/s10586-017-1366-6 Chriest, A., & Niles, M. (2018). The role of community social capital for food security following an extreme weather event. Journal of Rural Studies, 64, 80–90. https://doi.org/https://doi.org/10.1016/j.jrurstud.2018.09.019 Cinelli, M., Coles, S. R., & Kirwan, K. (2014). Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment. Ecological Indicators, 46, 138–148. https://doi.org/10.1016/j.ecolind.2014.06.011 Coco, A. A., Duhamel, C., & Santos, A. C. (2020). Modeling and solving the multi-period disruptions scheduling problem on urban networks. Annals of Operations Research, 285(1–2), 427–443. https://doi.org/10.1007/s10479-019-03248-5 Coello Coello, C. a, & Lamont, G. B. (2004). An introduction to multi-objective evolutionary algorithms and their applications. In C. A. C. Coello & G. B. Lamont (Eds.), Applications of Multi-Objective Evolutionary Algorithms: Advances in Natural Computation (Vol. 1, Issue 1, p. 792). World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/5712 Comité municipal para la gestión de riesgos y desastres de Roldanillo. (2020). Plan Municipal de Gestión del Riesgo - PMGR. Contreras, D., Blaschke, T., Kienberger, S., & Zeil, P. (2014). Myths and realities about the recovery of L׳Aquila after the earthquake. International Journal of Disaster Risk Reduction, 8, 125–142. https://doi.org/https://doi.org/10.1016/j.ijdrr.2014.02.001 Cozzolino, A., Rossi, S., & Conforti, A. (2012a). Agile and lean principles in the humanitarian supply chain: The case of the United Nations World Food Programme. Journal of Humanitarian Logistics and Supply Chain Management, 2(1), 16–33. https://doi.org/10.1108/20426741211225984 De Keyser, W., & Peeters, P. (1996). A note on the use of PROMETHEE multicriteria methods. European Journal of Operational Research, 89(3), 457–461. https://doi.org/10.1016/0377-2217(94)00307-6 Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. In IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (Vol. 6, Issue 2). Delgado-Antequera, L., Caballero, R., Sánchez-Oro, J., Colmenar, J. M., & Martí, R. (2020). Iterated greedy with variable neighborhood search for a multiobjective waste collection problem. Expert Systems with Applications, 145, 113101. https://doi.org/https://doi.org/10.1016/j.eswa.2019.113101 Donnini, M., Napolitano, E., Salvati, P., Ardizzone, F., Bucci, F., Fiorucci, F., Santangelo, M., Cardinali, M., & Guzzetti, F. (2017). Impact of event landslides on road networks: a statistical analysis of two Italian case studies. Landslides, 14(4), 1521–1535. https://doi.org/10.1007/s10346-017-0829-4 Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2019). Disaster relief operations: past, present and future. Annals of Operations Research, 283, 1–8. Duque, P. A. M., Dolinskaya, I. S., & Sorensen, K. (2016). Network repair crew scheduling and routing for emergency relief distribution problem. European Journal of Operational Research, 248(1), 272–285. https://doi.org/10.1016/j.ejor.2015.06.026 Edrissi, A., Nourinejad, M., & Roorda, M. J. (2015). Transportation network reliability in emergency response. Transportation Research Part E: Logistics and Transportation Review, 80, 56–73. https://doi.org/10.1016/j.tre.2015.05.005 Edwards, W., & Barron, F. H. (1994). Smarts and smarter: Improved simple methods for multiattribute utility measurement. In Organizational Behavior and Human Decision Processes (Vol. 60, Issue 3, pp. 306–325). https://doi.org/10.1006/obhd.1994.1087 Enrique, J., & Herrera, P. (2016). Estrategias de coordinación en operaciones de cargue y descargue de mercancías. Trabajo de grado de maestría. Universidad Nacional de Colombia, Bogotá D.C. Ergu, D., & Kou, G. (2012). Questionnaire design improvement and missing item scores estimation for rapid and efficient decision making. Annals of Operations Research, 197(1), 5–23. https://doi.org/10.1007/s10479-011-0922-3 Faiz, T. I., Vogiatzis, C., & Noor-E-Alam, M. (2019). A column generation algorithm for vehicle scheduling and routing problems. Computers and Industrial Engineering, 130(August 2018), 222–236. https://doi.org/10.1016/j.cie.2019.02.032 Fazel Zarandi, M. H., Davari, S., & Haddad Sisakht, S. A. (2012). The -coverage multiple allocation hub covering problem with mandatory dispersion. Scientia Iranica, 19(3), 902–911. https://doi.org/10.1016/j.scient.2012.03.007 Fikar, C., Gronalt, M., & Hirsch, P. (2016). A decision support system for coordinated disaster relief distribution. Expert Systems with Applications, 57, 104–116. https://doi.org/10.1016/j.eswa.2016.03.039 Frachtenberg, E. (2019). Practical Drone Delivery. Computer, 52(12), 53–57. https://doi.org/10.1109/MC.2019.2942290 García-Alviz, J., Galindo, G., Arellana, J., & Yie-Pinedo, R. (2021). Planning road network restoration and relief distribution under heterogeneous road disruptions. OR Spectrum, 43(4), 941–981. https://doi.org/10.1007/s00291-021-00644-x Giron, X., & Madrid, D. (2008). Comparación de Dos Métodos de Toma de Decisiones Multicriterio: MACBETH y PROMETHÉE (Spanish Version). Departament d’Enginyeria Informática i Matemátiques. Gossler, T., Wakolbinger, T., & Burkart, C. (2020). Outsourcing in humanitarian logistics – status quo and future directions. International Journal of Physical Distribution and Logistics Management, 50(4), 403–438. https://doi.org/10.1108/IJPDLM-12-2018-0400 Gossler, T., Wakolbinger, T., Nagurney, A., & Daniele, P. (2019). How to increase the impact of disaster relief: A study of transportation rates, framework agreements and product distribution. European Journal of Operational Research, 274(1), 126–141. https://doi.org/10.1016/j.ejor.2018.09.045 Grass, E., & Fischer, K. (2016). Two-stage stochastic programming in disaster management: A literature survey. Surveys in Operations Research and Management Science, 21(2), 85–100. https://doi.org/10.1016/j.sorms.2016.11.002 Grinberger, A. Y., & Felsenstein, D. (2016). Dynamic agent based simulation of welfare effects of urban disasters. Computers Environment and Urban Systems, 59, 129–141. https://doi.org/10.1016/j.compenvurbsys.2016.06.005 Groenendaal, J., Helsloot, I., & Scholtens, A. (2013). A Critical Examination of the Assumptions Regarding Centralized Coordination in Large-Scale Emergency Situations. Journal of Homeland Security and Emergency Management, 10(1). https://doi.org/10.1515/jhsem-2012-0053 Gschwind, T., Irnich, S., Tilk, C., & Emde, S. (2019). Branch-cut-and-price for scheduling deliveries with time windows in a direct shipping network. Journal of Scheduling. https://doi.org/10.1007/s10951-019-00620-y Guan, G., Lin, Z., Gong, Y., & Jiang, Z. (2020). Modeling and Simulation of Collaborative Dispatching of Disaster Relief Materials Based on Urgency. Mathematical Problems in Engineering, 2020, 1DUMMMY. https://doi.org/10.1155/2020/4274106 Guo, L., Li, Y. P., Huang, G. H., Wang, X. W., & Dai, C. (2012). Development of an Interval-Based Evacuation Management Model in Response to Nuclear-Power Plant Accident. Journal of Environmental Informatics, 20(2), 58–66. https://doi.org/10.3808/jei.201200220 Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351–366. https://doi.org/10.1016/j.ejor.2015.12.035 Guzmán Cortés, D. C., & Guerrero, W. J. (2018). Inventory routing models for humanitarian relief. In The 9th International Conference on Production Research – Americas 2018 (Vol. 1). Habib, M. S., Lee, Y. H., & Memon, M. S. (2016). Mathematical Models in Humanitarian Supply Chain Management: A Systematic Literature Review. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/3212095 Hammontree, E., Barrows, R., & Allen, B. (2000). Western federal lands highway division responds to northwest emergencies. Hasani, A., & Mokhtari, H. (2019). An integrated relief network design model under uncertainty: A case of Iran. Safety Science, 111, 22–36. https://doi.org/10.1016/j.ssci.2018.09.004 Helderop, E., & Grubesic, T. H. (2019). Flood evacuation and rescue: The identification of critical road segments using whole-landscape features. Transportation Research Interdisciplinary Perspectives, 3, 100022. https://doi.org/10.1016/j.trip.2019.100022 Henao, D., López, F., Chud Pantoja, V. L., & Osorio, J. C. (2019). Priorización multicriterio para la afiliación a un banco de alimentos en Colombia. Revista Logos, Ciencia & Tecnología, 12(1), 58–70. https://doi.org/10.22335/rlct.v12i1.1024 Holguin-Veras, J., Jaller, M., Van Wassenhove, L. N., Perez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of Operations Management, 30(7–8), 494–506. https://doi.org/10.1016/j.jom.2012.08.003 Hosseini, S. M. A., de la Fuente, A., & Pons, O. (2016). Multicriteria Decision-Making Method for Sustainable Site Location of Post-Disaster Temporary Housing in Urban Areas. Journal of Construction Engineering and Management, 142(9). https://doi.org/10.1061/(asce)co.1943-7862.0001137 Hu, F.-H., Feng, X., Zhao, M.-H., & Chen, Y.-R. (2015). Research on emergency supplies dispatching problem based on genetic algorithm. Open Cybernetics and Systemics Journal, 9(1), 62–68. 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 (English Edition), 6(5), 441–454. https://doi.org/10.1016/j.jtte.2019.07.001 Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Transportation Research Part E: Logistics and Transportation Review, 48(1), 2–18. https://doi.org/10.1016/j.tre.2011.05.004 Huizing, D., Schäfer, G., van der Mei, R. D., & Bhulai, S. (2020). The median routing problem for simultaneous planning of emergency response and non-emergency jobs. European Journal of Operational Research. Hwang, G., Han, J. H., & Chang, T. W. (2020). An integrated key performance measurement for manufacturing operations management. Sustainability (Switzerland), 12(13), 1–15. https://doi.org/10.3390/su12135260 Ibarra-Rojas, O. J., Hernandez, L., & Ozuna, L. (2018). The Accessibility Vehicle Routing Problem. Journal of Cleaner Production, 172, 1514–1528. https://doi.org/10.1016/j.jclepro.2017.10.249 Ibn Faiz, T., Vogiatzis, C., & Noor-E-Alam, M. (2019). A column generation algorithm for vehicle scheduling and routing problems. Computers & Industrial Engineering, 130, 222–236. https://doi.org/10.1016/j.cie.2019.02.032 Iloglu, S., & Albert, L. A. (2018). An integrated network design and scheduling problem for network recovery and emergency response. Operations Research Perspectives, 5(August), 218–231. https://doi.org/10.1016/j.orp.2018.08.001 Iloglu, S., & Albert, L. A. (2020). A maximal multiple coverage and network restoration problem for disaster recovery. Operations Research Perspectives. Immawan, T., Pratiwi, A. I., & Cahyo, W. N. (2019). The proposed dashboard model for measuring performance of Small-Medium Enterprises (SME). International Journal of Integrated Engineering, 11(5), 167–173. https://doi.org/10.30880/ijie.2019.11.05.021 Instituto Nacional de Vías (INVIAS) (2016). Manual de mantenimiento de carreteras. URL: https://www.invias.gov.co/index.php/archivo-y-documentos/documentos-tecnicos/7714-manual-de-mantenimiento-de-carreteras-2016-v2 Jahre, M., & Jensen, L. M. (2010). Coordination in humanitarian logistics through clusters. International Journal of Physical Distribution and Logistics Management, 40(8), 657–674. https://doi.org/10.1108/09600031011079319 Janiak, M. K., & Zak, J. (2014). Multiple criteria evaluation of different redesign variants of the public tram system. Transportation Research Procedia, 3(July), 690–699. https://doi.org/10.1016/j.trpro.2014.10.048 Jiang, J. P., Wang, P., Lung, W. S., Guo, L., & Li, M. (2012). A GIS-based generic real-time risk assessment framework and decision tools for chemical spills in the river basin. Journal of Hazardous Materials, 227, 280–291. https://doi.org/10.1016/j.jhazmat.2012.05.051 Jiang, Y. P., & Yuan, Y. F. (2019). Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges. International Journal of Environmental Research and Public Health, 16(5). https://doi.org/10.3390/ijerph16050779 Kalinowski, T., Matsypura, D., & Savelsbergh, M. W. P. (2015). Incremental network design with maximum flows. European Journal of Operational Research, 242(1), 51–62. https://doi.org/10.1016/j.ejor.2014.10.003 Kalkman, J. P., & de Waard, E. J. (2017). Inter-organizational disaster management projects: Finding the middle way between trust and control. International Journal of Project Management, 35(5), 889–899. https://doi.org/10.1016/j.ijproman.2016.09.013 Karakoc, D. B., Almoghathawi, Y., Barker, K., González, A. D., & Mohebbi, S. (2019). Community resilience-driven restoration model for interdependent infrastructure networks. International Journal of Disaster Risk Reduction, 38(June), 101228. https://doi.org/10.1016/j.ijdrr.2019.101228 Karakoc, D. B., Barker, K., Zobel, C. W., & Almoghathawi, Y. (2020). Social vulnerability and equity perspectives on interdependent infrastructure network component importance. Sustainable Cities and Society, 57(March 2019), 102072. https://doi.org/10.1016/j.scs.2020.102072 Khademi, N., Balaei, B., Shahri, M., Mirzaei, M., Sarrafi, B., Zahabiun, M., & Mohaymany, A. S. (2015). Transportation network vulnerability analysis for the case of a catastrophic earthquake. International Journal of Disaster Risk Reduction, 12, 234–254. https://doi.org/https://doi.org/10.1016/j.ijdrr.2015.01.009 Khan, M. U., Mesbah, M., Ferreira, L., & Williams, D. J. (2014). Development of road deterioration models incorporating flooding for optimum maintenance and rehabilitation strategies. Road & Transport Research, 23(1), 3–24. Kim, S., Shin, Y., Lee, G. M., & Moon, I. (2018). Network repair crew scheduling for short-term disasters. Applied Mathematical Modelling, 64, 510–523. https://doi.org/10.1016/j.apm.2018.07.047 Kovacs, G., & Moshtari, M. (2019). A roadmap for higher research quality in humanitarian operations: A methodological perspective. European Journal of Operational Research, 276(2), 395–408. https://doi.org/10.1016/j.ejor.2018.07.052 Kovacs, G., & Spens, K. (2009). Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management, 39(6), 506–528. https://doi.org/10.1108/09600030910985848 Kumar, B., & Bhaduri, S. (2018). Disaster risk in the urban villages of Delhi. International Journal of Disaster Risk Reduction, 31, 1309–1325. https://doi.org/https://doi.org/10.1016/j.ijdrr.2018.04.022 Laguna-Salvado, L., Lauras, M., Okongwu, U., & Comes, T. (2019). A multicriteria Master Planning DSS for a sustainable humanitarian supply chain. Annals of Operations Research, 283(1–2), 1303–1343. https://doi.org/10.1007/s10479-018-2882-3 Lamos Díaz, H., Aguilar Imitola, K., & Acosta Amado, R. J. (2019). Perspectivas de investigación en OR/MS en la gestión de operaciones de desastres: una revisión de la literatura. Revista Facultad De Ingeniería Universidad De Antioquia, (91), 43–59. https://doi.org/10.17533/udea.redin.n91a05 Larichev, O. I., & Moshkovich, H. M. (1995a). ZAPROS-LM - A method and system for ordering multiattribute alternatives. European Journal of Operational Research, 82(3), 503–521. https://doi.org/10.1016/0377-2217(93)E0143-L Leiras, A., de Brito Jr., I., Queiroz Peres, E., Rejane Bertazzo, T., & Yoshida Yoshizaki, H. (2014). Literature review of humanitarian logistics research: Trends and challenges. Journal of Humanitarian Logistics and Supply Chain Management, 4(1), 95-130. Disponible en: https://doi.org/10.1108/JHLSCM-04-2012-0008 Li, M. Y., Zhao, X. J., Fan, Z. P., Cao, P. P., & Qu, X. N. (2019). A model for assignment of rescuers considering multiple disaster areas. International Journal of Disaster Risk Reduction, 38(June), 101201. https://doi.org/10.1016/j.ijdrr.2019.101201 Li, P., Lan, H., & Saldanha-Da-Gama, F. (2019). A Bi-objective capacitated location-routing problem for multiple perishable commodities. IEEE Access. Lin, Y.-H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2011). A logistics model for emergency supply of critical items in the aftermath of a disaster. Socio-Economic Planning Sciences, 45(4), 132–145. https://doi.org/https://doi.org/10.1016/j.seps.2011.04.003 Liu, B., Sheu, J.-B., Zhao, X., Chen, Y., & Zhang, W. (2020). Decision Making on Post-Disaster Rescue Routing Problems from the Rescue Efficiency Perspective. European Journal of Operational Research. https://doi.org/https://doi.org/10.1016/j.ejor.2020.03.017 Liu, C., Kou, G., Peng, Y., & Alsaadi, F. E. (2019). Location-routing problem for relief distribution in the early post-earthquake stage from the perspective of fairness. Sustainability (Switzerland), 11(12). https://doi.org/10.3390/SU11123420 Liu, M., & Liang, J. (2013). Dynamic optimization model for allocating medical resources in epidemic controlling. Journal of Industrial Engineering and Management, 6(1), 73-88. http://dx.doi.org/10.3926/jiem.663 Liu, M., & Xiao, Y. (2015). Optimal scheduling of logistical support for medical resource with demand information updating. Mathematical Problems in Engineering, 2015. https://doi.org/10.1155/2015/765098 Liu, M., & Zhao, L. (2012). An integrated and dynamic optimisation model for the multi-level emergency logistics network in anti-bioterrorism system. International Journal of Systems Science, 43(8), 1464–1478. https://doi.org/10.1080/00207721.2010.547629 Liubartseva, S., Coppini, G., Pinardi, N., De Dominicis, M., Lecci, R., Turrisi, G., Cret, S., Martinelli, S., Agostini, P., Marra, P., & Palermo, F. (2016). Decision support system for emergency management of oil spill accidents in the Mediterranean Sea. Natural Hazards and Earth System Sciences, 16(8), 2009–2020. https://doi.org/10.5194/nhess-16-2009-2016 López-Ramírez, M. A., Rojas-Trejos, C. A., González-Velasco, J., Valle, U., & Industrial, E. D. I. (2017). Inventory control model using the Two - Bin System methodology in the hospital network of the City of Cali, Colombia. International Journal of Combinatorial Optimization Problems and Informatics, 8(2), 19–24. López-Vargas, J. C., & Cárdenas-Aguirre, D. M. (2018). Factores de influencia en la coordinación logística para la preparación y atención de desastres – Una revisión de literatura. Revista EIA, 15(30), 41–56. https://doi.org/10.24050/reia.v15i30.1146 Lorca, A., Celik, M., Ergun, O., & Keskinocak, P. (2017). An Optimization-Based Decision-Support Tool for Post-Disaster Debris Operations. Production and Operations Management, 26(6), 1076–1091. https://doi.org/10.1111/poms.12643 Loree, N., & Aros-Vera, F. (2018). Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics. Transportation Research Part E-Logistics and Transportation Review, 116, 1–24. https://doi.org/10.1016/j.tre.2018.05.003 Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R., Ahn, S. Y., AlMazroa, M. A., Alvarado, M., Anderson, H. R., Anderson, L. M., Andrews, K. G., Atkinson, C., Baddour, L. M., Barker-Collo, S., Bartels, D. H., … Murray, C. J. L. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2095–2128. https://doi.org/10.1016/S0140-6736(12)61728-0 Lu, G., Xiong, Y., Ding, C., & Wang, Y. (2016a). An optimal schedule for urban road network repair based on the greedy algorithm. PLoS ONE. Macea, L. F., Amaya, J., Cantillo, V., & Holguín-Veras, J. (2018). Evaluating economic impacts of water deprivation in humanitarian relief distribution using stated choice experiments. International Journal of Disaster Risk Reduction, 28(July 2017), 427–438. https://doi.org/10.1016/j.ijdrr.2018.03.029 Mahecha Peláez L.A., & Rojas Trejos, C. A. (2019). Propuesta de un modelo de localización de albergues temporales para la atención de damnificados en la fase de respuesta post desastre: caso inundación Roldanillo Valle Trabajo de grado para optar el título de Ingeniero Industrial - Universidad del Valle. Sede Zarzal. Maghsoudi, A., & Pazirandeh, A. (2016). Visibility, resource sharing and performance in supply chain relationships: insights from humanitarian practitioners. Supply Chain Management, 21(1), 125–139. https://doi.org/10.1108/SCM-03-2015-0102 Malone, T. W. (1987). Modeling Coordination in Organizations and Markets. Management Science, 33(10), 1317–1332. https://doi.org/10.1287/mnsc.33.10.1317 Manopiniwes, W., & Irohara, T. (2014). A review of relief supply chain optimization. Industrial Engineering and Management Systems, 13(1), 1-14. Mansoori, S., Bozorgi-Amiri, A., & Pishvaee, M. S. (2020). A robust multi-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties. Neural Computing and Applications. Mardani, A., Zavadskas, E. K., Govindan, K., Senin, A. A., & Jusoh, A. (2016a). VIKOR technique: A systematic review of the state of the art literature on methodologies and applications. Sustainability (Switzerland), 8(1), 1–38. https://doi.org/10.3390/su8010037 Marín Gómez, K. V., Morales Corredor J.D, & Rojas Trejos, C. A. (2018). Propuesta de un modelo de localización de albergues temporales y ruteo de personal especializado, para la atención de una población vulnerable ante un desastre de la región. Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal. Martínez López V.M., & Rojas Trejos, C. A. (2020). Propuesta de distribución de ayuda humanitaria considerando restricciones viales en desastres naturales súbitos. Caso de estudio: Roldanillo - Valle del Cauca Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal. Matsumaru, R., Nagami, K., & Takeya, K. (2012). Reconstruction of the Aceh Region following the 2004 Indian Ocean tsunami disaster: A transportation perspective. IATSS Research, 36(1), 11–19. https://doi.org/https://doi.org/10.1016/j.iatssr.2012.07.001 Maya Duque, P. A., Coene, S., Goos, P., Sörensen, K., & Spieksma, F. (2013). The accessibility arc upgrading problem. European Journal of Operational Research, 224(3), 458–465. https://doi.org/10.1016/j.ejor.2012.09.005 Maya Duque, P. A., Dolinskaya, I. S., & Sörensen, K. (2016). Network repair crew scheduling and routing for emergency relief distribution problem. European Journal of Operational Research, 248(1), 272–285. https://doi.org/10.1016/j.ejor.2015.06.026 Mendoza Patiño, D., Betancourt Cortez, F. J., Leguizamón Sierra, G., Dajles Lenis, D. E., Gámez Araújo, W. G., & Rojas Trejos, C. A. (2018a). Determination of the relevant factors in the outsourcing decision-making process in global supply chains applying the AHP methodology. Espacios, 39(41). Messa Mejía E., Sánchez Murilo T., Rojas Trejos, C. A., & González Velasco J. (2022) Propuesta de un modelo de distribución de vacunas considerando limitaciones de inventario en depósitos y puntos de entrega en el Valle del Cauca. Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal. Minas, J. P., Simpson, N. C., & Tacheva, Z. Y. (2020). Modeling emergency response operations: A theory building survey. Computers and Operations Research, 119, 104921. https://doi.org/10.1016/j.cor.2020.104921 Ministerio de Ambiente y Desarrollo Sostenible. (2012). Ley 1523: Sistema Nacional de Gestión del Riesgo. Instituto Distrital de Gestión Del Riesgo - IDIGER, 58. Manterola, C., Astudillo, P., Arias, E., & Claros, N. (2013). Systematic reviews of the literature: what should be known about them. Cirugía Española, 149-155. Mohammad, A., Rezapour, S, Baghaian, A. (2023). An integrative framework for coordination of damage assessment, road restoration, and relief distribution in disasters. Omega-International Journal of Management Science. https://doi.org/115 10.1016/j.omega.2022.102748 Montoya Calleja V., Ruiz Aguirre M.A., Guerra Ospina P.A., & Rojas Trejos, C. A. (2022, 5 de septiembre). Propuesta de un modelo de programación matemática para la localización de albergues temporales ante eventos de desastre. Caso de estudio: Roldanillo, Valle del Cauca, Colombia. Congreso Asociación Colombiana de Investigación Operativa - Institute of Industrial and Systems Engineers (ASOCIO-IISE) [Ponencia]. Universidad de La Sabana, Universidad Sergio Arboleda, Universidad de los Andes. Bogotá D.C, Colombia. Moreno, A., Alem, D., Gendreau, M., & Munari, P. (2020). The heterogeneous multicrew scheduling and routing problem in road restoration. Transportation Research Part B: Methodological, 141, 24–58. https://doi.org/10.1016/j.trb.2020.09.002 Moreno, A., Munari, P., & Alem, D. (2019). A branch-and-Benders-cut algorithm for the Crew Scheduling and Routing Problem in road restoration. European Journal of Operational Research, 275(1), 16–34. https://doi.org/10.1016/j.ejor.2018.11.004 Morshedlou, N., González, A. D., & Barker, K. (2018). Work crew routing problem for infrastructure network restoration. Transportation Research Part B: Methodological, 118, 66–89. https://doi.org/10.1016/j.trb.2018.10.001 Mostajabdaveh, M., Gutjahr, W. J., & Salman, F. S. (2019). Inequity-averse shelter location for disaster preparedness. Iise Transactions, 51(8), 809–829. https://doi.org/10.1080/24725854.2018.1496372 Mutebi, H., Muhwezi, M., Ntayi, J. M., & Munene, J. C. K. (2020). Organisation size, innovativeness, self-organisation and inter-organisational coordination. International Journal of Emergency Services, 9(3), 359–394. https://doi.org/10.1108/IJES-05-2020-0024 Nassereddine, M., Azar, A., Rajabzadeh, A., & Afsar, A. (2019). Decision making application in collaborative emergency response: A new PROMETHEE preference function. International Journal of Disaster Risk Reduction, 38. https://doi.org/10.1016/j.ijdrr.2019.101221 Nurmala, N., de Leeuw, S., & Dullaert, W. (2017). Humanitarian–business partnerships in managing humanitarian logistics. Supply Chain Management, 22(1), 82–94. https://doi.org/10.1108/SCM-07-2016-0262 Nurre, S. G., Cavdaroglu, B., Mitchell, J. E., Sharkey, T. C., & Wallace, W. A. (2012a). Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem. European Journal of Operational Research, 223(3), 794–806. https://doi.org/10.1016/j.ejor.2012.07.010 Osorio, A., Brailsford, S. & Smith, H. (2014). Un modelo de optimización bi-objetivo para la selección de tecnología y asignación de donantes en la cadena de suministro de sangre. Revista S&T, 12(30), 9-24 Osorio, C.A. (2016). Mecanismos de coordinación para la optimización del desempeño de la cadena logística humanitaria mediante modelamiento estocástico. Caso colombiano. Tesis de doctorado. Universidad Nacional de Colombia, Bogotá D.C. Ozdamar, L., Aksu, D. T., Yasa, E., & Ergunes, B. (2018). Disaster relief routing in limited capacity road networks with heterogeneous flows. Journal of Industrial and Management Optimization. Özdamar, L., & Ertem, M. A. (2015). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55–65. https://doi.org/10.1016/j.ejor.2014.11.030 Özdamar, L., Tüzün Aksu, D., & Ergüneş, B. (2014). Coordinating debris cleanup operations in post disaster road networks. Socio-Economic Planning Sciences, 48(4), 249–262. https://doi.org/10.1016/j.seps.2014.08.001 Papamichail, K. N., & French, S. (2013). 25 Years of MCDA in nuclear emergency management. Ima Journal of Management Mathematics, 24(4), 481–503. https://doi.org/10.1093/imaman/dps028 Paraskevopoulos, D. C., Laporte, G., Repoussis, P. P., & Tarantilis, C. D. (2017). Resource constrained routing and scheduling: Review and research prospects. European Journal of Operational Research. Paul, J. A., & MacDonald, L. (2016). Optimal location, capacity and timing of stockpiles for improved hurricane preparedness. International Journal of Production Economics, 174, 11–28. https://doi.org/10.1016/j.ijpe.2016.01.006 Perez-Rodriguez, N., & Holguin-Veras, J. (2016). Inventory-Allocation Distribution Models for Postdisaster Humanitarian Logistics with Explicit Consideration of Deprivation Costs. Transportation Science, 50(4), 1261–1285. https://doi.org/10.1287/trsc.2014.0565 Podgórski, D. (2015). Measuring operational performance of OSH management system - A demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146–166. https://doi.org/10.1016/j.ssci.2014.11.018 Prabawa, P., & Choi, D.-H. (2020). Multi-Agent Framework for Service Restoration in Distribution Systems with Distributed Generators and Static/Mobile Energy Storage Systems. IEEE Access, 8, 51736–51752. https://doi.org/10.1109/ACCESS.2020.2980544 Pradhananga, R., Mutlu, F., Pokharel, S., Holguin-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 Prakash, C., Besiou, M., Charan, P., & Gupta, S. (2020). Organization theory in humanitarian operations: a review and suggested research agenda. Journal of Humanitarian Logistics and Supply Chain Management, 10(2), 261–284. https://doi.org/10.1108/JHLSCM-08-2019-0051 Rais, A., Alvelos, F., & Carvalho, M. S. (2014). New mixed integer-programming model for the pickup-and-delivery problem with transshipment. European Journal of Operational Research, 235(3), 530–539. https://doi.org/https://doi.org/10.1016/j.ejor.2013.10.038 Ramírez Drada, E., Chud Pantoja, V. L., & Orejuela Cabrera, J. P. (2019). Propuesta metodológica multicriterio para la distribución semicontinua de plantas. Suma de Negocios, 10(23), 132–145. https://doi.org/10.14349/sumneg/2019.v10.n23.a6 Ransikarbum, K., & Mason, S. J. (2016). Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration. International Journal of Production Economics, 182, 324–341. https://doi.org/10.1016/j.ijpe.2016.08.030 Reddy, G. H., Chakrapani, P., Goswami, A. K., & Choudhury, N. B. D. (2017a). Fuzzy Based Approach for Restoration of Distribution System during Post Natural Disasters. IEEE Access, 6, 3448–3458. https://doi.org/10.1109/ACCESS.2017.2779823 Regis-Hernandez, F., Mora-Vargas, J., & Ruiz, A. (2017). A Multi-Criteria Vertical Coordination Framework for a Reliable Aid Distribution. Journal of Industrial Engineering and Management-Jiem, 10(4), 789–815. https://doi.org/10.3926/jiem.2253 Resolución No. 1400 (8 de junio de 2004). Ministerio de Transporte. Colombia. Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Cheikhrouhou, N., & Taheri-Moghaddam, A. (2016a). An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management. Transportation Research Part E-Logistics and Transportation Review, 93, 485–509. https://doi.org/10.1016/j.tre.2016.07.003 Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Bozorgi-Amiri, A. (2016b). An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Computers and Industrial Engineering, 94, 201–215. https://doi.org/10.1016/j.cie.2016.01.014 Ríos, R. C. G., Sánchez, C. N. G., & González, E. L. R. (2019). Application of the scoring method for integral customer classification. Ingeniare, 27(3), 375–382. https://doi.org/10.4067/S0718-33052019000300375 Rivera, J. G. (2010). La comunicación del riesgo: hacia un modelo efectivo y situacional (ITM, Ed.). ITM. Rodriguez-Espindola, Oscar, Albores, P., & Brewster, C. (2018). Dynamic formulation for humanitarian response operations incorporating multiple organisations. International Journal of Production Economics, 204(August), 83–98. https://doi.org/10.1016/j.ijpe.2018.07.023 Rodríguez Taborda J.D., Gómez Hernández J.M., & Rojas Trejos, C. A. (2022). Propuesta de un modelo de programación de recursos de reparación para disrupciones viales transitorias por desastres naturales súbitos. Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal. Rojas Trejos, C. A., & González Velasco, J. (2018). A Multicriteria Location Model for a Solid Waste Disposal Center in Valle Del Cauca, Colombia. Supply Chain Management and Logistics in Latin America, 37–53. https://doi.org/10.1108/978-1-78756-803-720181004 Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2023a). Humanitarian aid distribution logistics with accessibility constraints: a systematic literature review. In Journal of Humanitarian Logistics and Supply Chain Management (Vol. 13, Issue 1, pp. 26–41). Emerald Publishing. https://doi.org/10.1108/JHLSCM-05-2021-0041 Rojas Trejos, C. A., Meisel, J. D., Adarme Jaimes, W., & Orejuela Cabrera J. P. (2023b, 30 de marzo). Distribución de ayuda humanitaria considerando disrupciones viales transitorias [Conferencia]. XXVI – II Seminario de Logística. Grupo de Investigación, Sociedad, Economía y Productividad (SEPRO). Universidad Nacional de Colombia. Bogotá D.C, Colombia. Rojas Trejos, C. A., Meisel, J. D., Adarme Jaimes, W., & Orejuela Cabrera J. P. (2022a, 22 de noviembre). Programación de recursos de reparación para la atención de disrupciones viales transitorias en logística humanitaria [Conferencia]. XXV - IV Seminario de Logística. Grupo de Investigación, Sociedad, Economía y Productividad (SEPRO). Universidad Nacional de Colombia. Bogotá D.C, Colombia. Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2021, 22 de agosto). Assignment, location and routing in humanitarian logistics [Ponencia]. 22nd Conference of the International Federation of Operational Research Societies (IFORS). Seúl, Corea del Sur. Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2022b). Enfoque integrado multicriterio para la medición del desempeño en redes de distribución de ayuda humanitaria [Ponencia]. IX Congreso Internacional de Industria y Organizaciones. Universidad Nacional de Colombia. Bogotá D.C, Colombia. Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2023c). X Congreso Internacional de Industria y Organizaciones. Universidad de la Costa, Universidad Nacional de Colombia [Ponencia]. X Congreso Internacional de Industria y Organizaciones. Universidad Nacional de Colombia. Barranquilla, Colombia. Rojas Trejos, C. A., Saavedra Arango, L. F., & Orejuela Cabrera, J. P. (2017). Scheduling of internal materials supply operations in a pre-milling type concentrate food plant. Producción + Limpia, 12(2), 147–158. https://doi.org/10.22507/pml.v12n2a12 Rojas-trejos, C. A., González-velasco, J., López-ramírez, M. A., Valle, U., & Industrial, E. D. I. (2017). Optimization Model for the Location of Prehospital Care Ambulances in the city of Cali Colombia. International Journal of Combinatorial Optimization Problems and Informatics, 8(3), 64–70. Roozbahani, A., Zahraie, B., & Tabesh, M. (2012a). PROMETHEE with Precedence Order in the Criteria (PPOC) as a New Group Decision Making Aid: An Application in Urban Water Supply Management. Water Resources Management, 26(12), 3581–3599. https://doi.org/10.1007/s11269-012-0091-4 Saaty, R. W. (1987). The analytic hierarchy process-what it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8 Sabbaghtorkan, M., Batta, R., & He, Q. (2020). Prepositioning of assets and supplies in disaster operations management: Review and research gap identification. European Journal of Operational Research, 284(1), 1–19. https://doi.org/10.1016/j.ejor.2019.06.029 Sabouhi, F., Bozorgi-Amiri, A., & Vaez, P. (2020). Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty. Kybernetes, 50(9), 2632–2650. https://doi.org/10.1108/K-10-2020-0632 Safarpour, H., Fooladlou, S., Safi-Keykaleh, M., Mousavipour, S., Pirani, D., Sahebi, A., Ghodsi, H., Farahi-Ashtiani, I., & Dehghani, A. (2020). Challenges and barriers of humanitarian aid management in 2017 Kermanshah earthquake: A qualitative study. BMC Public Health, 20(1), 1–10. https://doi.org/10.1186/s12889-020-08722-5 Sahin, H., Kara, B. Y., & Karasan, O. E. (2016). Debris removal during disaster response: A case for Turkey. Socio-Economic Planning Sciences, 53, 49–59. https://doi.org/10.1016/j.seps.2015.10.003 Sakiani, R., Seifi, A., & Khorshiddoust, R. R. (2020). Inventory routing and dynamic redistribution of relief goods in post-disaster operations. Computers and Industrial Engineering, 140(December 2019), 106219. https://doi.org/10.1016/j.cie.2019.106219 Sakuraba, C. S., Santos, A. C., & Prins, C. (2016a). Work-troop scheduling for road network accessibility after a major earthquake. Electronic Notes in Discrete Mathematics. Sakuraba, C. S., Santos, A. C., Prins, C., Bouillot, L., Durand, A., & Allenbach, B. (2016b). Road network emergency accessibility planning after a major earthquake. EURO Journal on Computational Optimization. Sánchez Pedraza, R., & Jaramillo González, L. (2009). Methodology of Qualification and Summary of the Opinions in Formal Consensus. Revista Colombiana de Psiquiatría, 38(4), 777–786. Sanci, E., & Daskin, M. S. (2019). Integrating location and network restoration decisions in relief networks under uncertainty. European Journal of Operational Research, 279(2), 335–350. https://doi.org/10.1016/j.ejor.2019.06.012 Sarache, W. A., Cárdenas, D. M., & Giraldo, J. A. (2005). Procedimiento para la definición y jerarquización de prioridades competitivas de fabricación. Aplicaciones en las pymes de la industria metalmecánica. Ingeniería y Competitivad, 7(2), 84–91. Sarkissian, R. Der, Zaninetti, J.-M., & Abdallah, C. (2019). The use of geospatial information as support for Disaster Risk Reduction; contextualization to Baalbek-Hermel Governorate/Lebanon. Applied Geography, 111, 102075. https://doi.org/https://doi.org/10.1016/j.apgeog.2019.102075 Sc, A. M., Ramos, F., & Auxiliar, P. (2011). Una aproximación a la toma de decisiones en el escenario azucarero mediante el método Delphi . Un estudio de caso . An approximation to the take of decision at the sugar scenario , using the method Delphi . A case study . Revista centro azúcar, 38(3), 39–45. Shanshan, H., Wenhai, Z., & Chaofeng, W. (2015). Collaborative dynamic optimization on post-earthquake emergency supply and road rehabilitation based on road network connectivity. Open Automation and Control Systems Journal. Sharma, A., Singh, P. K., Sharma, A., & Kumar, R. (2019). An efficient architecture for the accurate detection and monitoring of an event through the sky. Computer Communications, 148, 115–128. https://doi.org/https://doi.org/10.1016/j.comcom.2019.09.009 Shendarkar, A., Vasudevan, K., Lee, S., & Son, Y. J. (2008). Crowd simulation for emergency response using BDI agents based on immersive virtual reality. Simulation Modelling Practice and Theory, 16(9), 1415–1429. https://doi.org/10.1016/j.simpat.2008.07.004 Shin, Y., Kim, S., & Moon, I. (2019). Integrated optimal scheduling of repair crew and relief vehicle after disaster. Computers and Operations Research, 105, 237–247. https://doi.org/10.1016/j.cor.2019.01.015 Silva, L. de O., Bandeira, R. A. de M., & Campos, V. B. G. (2019a). Proposal to planning facility location using UAV and geographic information systems in a post-disaster scenario. International Journal of Disaster Risk Reduction, 36(December 2018), 101080. https://doi.org/10.1016/j.ijdrr.2019.101080 Sirikijpanichkul, A., Van Dam, Koen H., Ferreira, L., & Lukszo, Z. (2007). Optimizing the Location of Intermodal Freight Hubs: An Overview of the Agent Based Modelling Approach. Journal of Transportation Systems Engineering and Information Technology, 7(4), 71–81. https://doi.org/10.1016/S1570-6672(07)60031-2 Solmaz, G., & Turgut, D. (2017). Modeling pedestrian mobility in disaster areas. Pervasive and Mobile Computing, 40, 104–122. https://doi.org/https://doi.org/10.1016/j.pmcj.2017.05.005 Solomatin, A. (2018). The World Bank and Fragile States: Dynamics of cooperation and aid structure. International Organisations Research Journal, 13(1), 114–132. https://doi.org/10.17323/1996-7845-2018-01-07 Sörensen, K., & Vanovermeire, C. (2013). Bi-objective optimization of the intermodal terminal location problem as a policy-support tool. Computers in Industry, 64(2), 128–135. https://doi.org/10.1016/j.compind.2012.10.012 Steenbergen, R., Mes, M., Heeswijk, W. (2023). Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty. Transportation Research Part C: Emerging Technologies, 157, 1–28. https://doi.org/10.1016/j.trc.2023.104401 Tamima, U., & Chouinard, L. (2016). Development of evacuation models for moderate seismic zones: A case study of Montreal. International Journal of Disaster Risk Reduction, 16, 167–179. https://doi.org/https://doi.org/10.1016/j.ijdrr.2016.02.003 Tan, Y., Qiu, F., Das, A. K., Kirschen, D. S., Arabshahi, P., & Wang, J. (2019). Scheduling Post-Disaster Repairs in Electricity Distribution Networks. IEEE Transactions on Power Systems. Tatham, P., & Spens, K. (2011). Towards a humanitarian logistics knowledge management system. Disaster Prevention and Management, 20(1), 6–26. https://doi.org/10.1108/09653561111111054 Tatiana, F., & Duarte, M. (2017). Mecanismo de coordinación entre actores públicos y privados del sistema de transporte de carga de Colombia. Tesis de maestría. Universidad Nacional de Colombia, Bogotá D.C. Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations. Socio-Economic Planning Sciences, 64(October 2017), 21–37. https://doi.org/10.1016/j.seps.2017.12.004 Turgut, B. T., Tas, G., Herekoglu, A., Tozan, H., & Vayvay, O. (2011). A fuzzy AHP based decision support system for disaster center location selection and a case study for Istanbul. Disaster Prevention and Management, 20(5), 499–520. https://doi.org/10.1108/09653561111178943 Tuzun Aksu, D., & Ozdamar, L. (2014). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E: Logistics and Transportation Review, 61, 56–67. https://doi.org/10.1016/j.tre.2013.10.009 Tzeng, G. H., Cheng, H. J., & Huang, T. D. (2007). Mufti-objective optimal planning for designing relief delivery systems. Transportation Research Part E-Logistics and Transportation Review, 43(6), 673–686. https://doi.org/10.1016/j.tre.2006.10.012 Unidad Nacional para la Gestión de Riesgos de Desastres, U. (2013). Estandarización de ayuda humanitaria. Unidad Nacional para la Gestión del Riesgo de Desastres. (2020). Consolidado Atención de Emergencias. http://portal.gestiondelriesgo.gov.co/Paginas/Consolidado-Atencion-de-Emergencias.aspx Unidad Nacional para la Gestión del Riesgo de Desastres. (2021). Consolidado Atención de Emergencias. http://portal.gestiondelriesgo.gov.co/Paginas/Consolidado-Atencion-de-Emergencias.aspx Upayokin, A., Mattingly, S. P., & Lugo-Serrato, S. A. (2008). Decision-making procedure for assessing performance measures of freeway operations. Transportation Research Record, 2046, 11–19. https://doi.org/10.3141/2046-02 Usón, T. J., Klonner, C., & Höfle, B. (2016). Using participatory geographic approaches for urban flood risk in Santiago de Chile: Insights from a governance analysis. Environmental Science & Policy, 66, 62–72. https://doi.org/https://doi.org/10.1016/j.envsci.2016.08.002 Vahdani, B., Veysmoradi, D., Noori, F., & Mansour, F. (2018a). Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. International Journal of Disaster Risk Reduction, 27(October 2017), 290–306. https://doi.org/10.1016/j.ijdrr.2017.10.015 Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi, S. M. (2018b). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications, 30(3), 1–41. https://doi.org/10.1007/s00521-016-2696-7 Vahdani, B., Veysmoradi, D., Shekari, N., & Amiri, M. (2022). Planning for relief distribution, victim evacuation, redistricting and service sharing under uncertainty. Socio-Economic Planning Sciences, 80, 835–854. https://doi.org/10.1016/j.seps.2021.101158 Vahdat, K., Smith, N. J., & Amiri, G. G. (2014). Fuzzy multicriteria for developing a risk management system in seismically prone areas. Socio-Economic Planning Sciences, 48(4), 235–248. https://doi.org/10.1016/j.seps.2014.05.002 Vega, D., & Roussat, C. (2015). Humanitarian logistics: The role of logistics service providers. International Journal of Physical Distribution and Logistics Management, 45(4), 352–375. https://doi.org/10.1108/IJPDLM-12-2014-0309 Velasquez, J. D., Yoon, S. W., & Nof, S. Y. (2010). Computer-based collaborative training for transportation security and emergency response. Computers in Industry, 61(4), 380–389. https://doi.org/10.1016/j.compind.2009.12.007 Venkatesh, V. G., Zhang, A., Deakins, E., Luthra, S., & Mangla, S. (2019a). A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains. Annals of Operations Research, 283(1–2), 1517–1550. https://doi.org/10.1007/s10479-018-2981-1 Veysmoradi, D., Vahdani, B., Farhadi Sartangi, M., & Mousavi, S. M. (2018). Multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty. Scientia Iranica, 25(6), 3635-3653. Victoria, J. F., Afsar, H. M., & Prins, C. (2016). Column Generation based heuristic for the Vehicle Routing Problem with Time-Dependent Demand. IFAC-PapersOnLine, 49(12), 526–531. https://doi.org/10.1016/j.ifacol.2016.07.684 Vodák, R., Bíl, M., & Křivánková, Z. (2018). A modified ant colony optimization algorithm to increase the speed of the road network recovery process after disasters. International Journal of Disaster Risk Reduction, 31(April), 1092–1106. https://doi.org/10.1016/j.ijdrr.2018.04.004 Wang, J. W., Wang, H. F., Zhou, Y. M., Wang, Y., & Zhang, W. J. (2019). On an integrated approach to resilient transportation systems in emergency situations. Natural Computing, 18(4), 815–823. https://doi.org/10.1007/s11047-016-9605-y Wang, Y. N., Liang, Y. Z., & Sun, H. (2020). A Regret Theory-Based Decision-Making Method for Urban Rail Transit in Emergency Response of Rainstorm Disaster. Journal of Advanced Transportation, 2020. https://doi.org/10.1155/2020/3235429 Wijnmalen, D. J. D. (2007). Analysis of benefits, opportunities, costs, and risks (BOCR) with the AHP-ANP: A critical validation. Mathematical and Computer Modelling, 46(7–8), 892–905. https://doi.org/10.1016/j.mcm.2007.03.020 Wohlgemuth, S., Oloruntoba, R., & Clausen, U. (2012). Dynamic vehicle routing with anticipation in disaster relief. Socio-Economic Planning Sciences, 46(4), 261–271. https://doi.org/10.1016/j.seps.2012.06.001 Wu, J., & Wang, P. (2020). Post-disruption performance recovery to enhance resilience of interconnected network systems. Sustainable and Resilient Infrastructure. Wu, P., Cheng, J., & Feng, C. (2019). Resource-Constrained Emergency Scheduling for Forest Fires with Priority Areas: An Efficient Integer-Programming Approach. IEEJ Transactions on Electrical and Electronic Engineering, 14(2), 261–270. https://doi.org/10.1002/tee.22805 Yan, J., Hu, B., Xie, K., Tai, H. M., & Li, W. (2020). Post-disaster power system restoration planning considering sequence dependent repairing period. International Journal of Electrical Power and Energy Systems, 117(August 2019). https://doi.org/10.1016/j.ijepes.2019.105612 Yan, S., Chu, J. C., & Shih, Y.-L. (2014). Optimal scheduling for highway emergency repairs under large-scale supply-demand perturbations. IEEE Transactions on Intelligent Transportation Systems. Ye, Y., Liu, N., Hu, G. P., & Zhan, S. L. (2016). Scheduling algorithm based on follow-up sharing character for post-event response resource distribution in large-scal disasters. Journal of Systems Science and Systems Engineering, 25(1), 77–101. https://doi.org/10.1007/s11518-015-5284-y Ye, Y., Liu, N., & Zhan, S.-L. (2013). Information update based sequential approach for emergency resources allocation planning. Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science). Yilmaz, H., & Kabak, Ö. (2020). Prioritizing distribution centers in humanitarian logistics using type-2 fuzzy MCDM approach. Journal of Enterprise Information Management, 33(5), 1199–1232. https://doi.org/10.1108/JEIM-09-2019-0310 Yoon, K. P., & Kim, W. K. (2017a). The behavioral TOPSIS. Expert Systems with Applications, 89(December), 266–272. https://doi.org/10.1016/j.eswa.2017.07.045 YV, Y. H., & DA, D. W. (1971). On a bicriterion formation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man and Cybernetics, 296–297. Zafri, N. M., Sameen, I., Jahangir, A., Tabassum, N., & Hasan, M. M. U. (2020). A multi-criteria decision-making approach for quantification of accessibility to market facilities in rural areas: an application in Bangladesh. GeoJournal, 0123456789. https://doi.org/10.1007/s10708-020-10161-z Zagorecki, A., Ko, K., & Comfort, L. K. (2010). Interorganizational Information Exchange and Efficiency: Organizational Performance in Emergency Environments. Jasss-the Journal of Artificial Societies and Social Simulation, 13(3). https://doi.org/10.18564/jasss.1589 Zhan, S. L., Liu, N., & Ye, Y. (2014). Coordinating efficiency and equity in disaster relief logistics via information updates. International Journal of Systems Science, 45(8), 1607–1621. https://doi.org/10.1080/00207721.2013.777490 Zhang, C., Liu, X., Jiang, Y., Fan, B., & Song, X. (2016). A two-stage resource allocation model for lifeline systems quick response with vulnerability analysis. European Journal of Operational Research, 250(3), 855–864. https://doi.org/10.1016/j.ejor.2015.10.022 Zhang, M.-X., Zhang, B., & Zheng, Y.-J. (2014). Bio-inspired meta-heuristics for emergency transportation problems. Algorithms, 7(1), 15–31. https://doi.org/10.3390/a7010015 Zhang, P. Y., Liu, Y. K., Yang, G. Q., & Zhang, G. Q. (2020). A distributionally robust optimization model for designing humanitarian relief network with resource reallocation. Soft Computing, 24(4), 2749–2767. https://doi.org/10.1007/s00500-019-04362-z Zhang, Z. K., Zhu, C. F., Wang, Q. R., & Yuan, J. S. (2019). Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory. Journal of Advanced Transportation, 2, 1-18. https://doi.org/10.1155/2019/2728073 Zhou, Q., Huang, W. L., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science, 49(2), 243–252. https://doi.org/10.1016/j.ssci.2010.08.005 Zhou, Y., Liu, J., Zhang, Y., & Gan, X. (2017). A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review, 99, 77–95. https://doi.org/https://doi.org/10.1016/j.tre.2016.12.011 Zitzler, E., & Thiele, L. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. In IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (Vol. 3, Issue 4). Zou, Q., & Chen, S. (2020). Resilience Modeling of Interdependent Traffic-Electric Power System Subject to Hurricanes. Journal of Infrastructure Systems, 26(1). https://doi.org/10.1061/(ASCE)IS.1943-555X.0000524 Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158–181. https://doi.org/10.1016/j.eswa.2017.02.016 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Meisel Donoso, José David42e54003d74312b1570ad01b0b33c5caAdarme Jaimes, Wilson0c908dec9d14c2b6312f913bc35a53ac600Rojas Trejos, Carlos Albertoc6bcdf63e9fc4d2f5be3c74a7a2b7d37600Sociedad, Economía y Productividad - SEPRO2024-04-09T19:30:35Z2024-04-09T19:30:35Z2024-03-01https://repositorio.unal.edu.co/handle/unal/85888Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasA raíz de los desastres naturales súbitos presentados en el mundo, se ha demostrado que la tasa de supervivencia de la población se relaciona de forma directa con la entrega de suministros. Las 72 horas que siguen al evento disruptivo pueden considerarse el período después del cual la probabilidad de supervivencia de la población puede disminuir drásticamente, cuando el grado de dificultad que las personas o comunidades tienen para satisfacer sus necesidades sociales y económicas básicas tiende a aumentar por limitaciones de accesibilidad en la red de carreteras. En la literatura se han identificado dos enfoques para abordar el problema de entrega de ayudas humanitarias en zonas con limitaciones de accesibilidad denominados distribución de ayuda y restauración del acceso. El primero consiste en encontrar caminos transitables para que los equipos de ayuda lleguen a la población, y el segundo genera un cronograma de reparación para mejorar el acceso a las áreas de refugiados o puntos de demanda. En la literatura se han abordado ambos problemas de forma aislada, a través del desarrollo de modelos de toma de decisiones que ignoran la interrelación que poseen ambos procesos y su impacto en medidas de desempeño en conflicto relacionadas con la eficiencia (minimización de costos operacionales), eficacia (minimización del tiempo de respuesta) y bienestar social (minimización del tiempo o costo de privación de la población afectada). Esta investigación consideró el diseño de rutas para entrega de ayuda humanitaria en zonas con limitaciones de acceso, analizando el impacto que tiene la afectación de la malla vial en las decisiones de entregas de ayudas humanitarias y restauración de las vías. Posteriormente, se desarrolló una propuesta de coordinación entre ambos procesos soportada a través de la formulación de un modelo matemático multiobjetivo que facilitó el proceso de toma de decisiones logísticas. Como conclusión general la investigación determinó que el proceso de distribución de la ayuda humanitaria puede tener en cuenta las limitaciones derivadas de la operación de reparación de la infraestructura vial, basándose en la representación de ventanas temporales en las que las carreteras están disponibles como consecuencia de la reparación temporal de las mismas, lo que demuestra que el avance en la reparación de las carreteras tiene un impacto directo en la configuración de las rutas y en el tiempo total de llegada de la ayuda humanitaria a los puntos de demanda. Por otra parte, los resultados obtenidos demostraron también que, en la programación de los recursos de reparación, es necesario considerar las condiciones de precedencia y simultaneidad en los tiempos de arribo y salida de recursos de reparación según las características de la disrupción vial, aspecto que se ha pasado por alto en la literatura. Como aporte general, la investigación diseñó una propuesta de coordinación entre la distribución de ayuda humanitaria y la restauración transitoria de las vías, basada en la decisión conjunta y colaborativa en el desarrollo de actividades enmarcadas a nivel operativo durante la fase de respuesta; considerando múltiples medidas de desempeño relacionadas con las dimensiones de eficiencia, eficacia y bienestar social bajo un enfoque multiobjetivo. Igualmente, como aporte adicional, se consideraron las relaciones de interdependencia de recursos limitados y el establecimiento de tiempos de llegada y salida de vehículos en los puntos de demanda, considerando tiempos de finalización de las operaciones de reparación, representando la relación entre recursos en los procesos distribución y reparación. Finalmente, se estableció la minimización de la tardanza máxima en la entrega de ayuda humanitaria como medida de desempeño equivalente con la minimización del tiempo de privación dentro de la dimensión de bienestar social. (Texto tomado de la fuente).In the wake of sudden-onset natural disasters around the world, it has been shown that the survival rate of the population is directly related to the delivery of supplies. The 72 hours following the disruptive event can be considered the period after which the probability of population survival can decrease drastically, when the degree of difficulty that individuals or communities have in meeting their basic social and economic needs tends to increase due to accessibility limitations in the road network. Two approaches have been identified in the literature to address the problem of delivering humanitarian aid in areas with accessibility constraints: aid distribution and access restoration. The first involves finding passable roads aid teams to reach the population, and the second generates a repair schedule to improve access to refugee areas or demand points. The literature has addressed both problems in isolation, developing decision-making models that ignore the interrelationship of the two processes and their impact on conflicting performance measures related to efficiency (minimization of operational costs), effectiveness (minimization of response time) and social welfare (minimization of the time or cost of deprivation of the affected population). This research considered the design of routes for the delivery of humanitarian aid in areas with access limitations, analyzing the impact of the road network on the decisions of humanitarian aid delivery and road restoration. Subsequently, a coordination proposal was developed between both processes supported through the formulation of a multi-objective mathematical model that facilitated the logistical decision-making process. As a general conclusion, the research determined that the humanitarian aid distribution process can take into account the limitations derived from the road infrastructure repair operation, based on the representation of temporary windows in which roads are available as a consequence of temporary road repair, which shows that the progress in road repair has a direct impact on the configuration of the routes and on the total time of arrival of humanitarian aid to the points of demand. On the other hand, the results obtained also showed that, in the programming of repair resources, it is necessary to consider the conditions of precedence and simultaneity in the arrival and departure times of repair resources according to the characteristics of the road disruption, an aspect that has been overlooked in the literature. As a general contribution, the research designed a coordination proposal between the distribution of humanitarian aid and the temporary restoration of roads, based on joint and collaborative decisions in the development of activities framed at the operational level during the response phase; considering multiple performance measures related to the dimensions of efficiency, efficacy, and social welfare under a multi-objective approach. Likewise, as an additional contribution, the interdependence relationships of limited resources and the establishment of arrival and departure times of vehicles at the demand points were considered, considering completion times of repair operations, representing the relationship between resources in the distribution and repair processes. Finally, the minimization of the maximum delay in the delivery of humanitarian aid was established as a performance measure equivalent to the minimization of deprivation time within the social welfare dimension.Programa de Becas de Excelencia Doctoral Bicentenario del Ministerio de Ciencia, Tecnología e Innovación. Colombia.DoctoradoDoctor en IngenieríaEl proyecto de tesis doctoral se enmarcó en tres fases. En la fase 1 se realizó una caracterización del sistema de distribución de ayuda humanitaria y el proceso de restauración del acceso . En la fase 2 se presentó la formulación de la propuesta de coordinación de la distribución de ayuda y restauración del acceso, haciendo uso de un modelo de programación matemática. Por último, la fase 4 consistió en la validación de la propuesta de coordinación a partir de un contexto geográfico en Colombia.Métodos y modelos de optimización y estadística en ingeniería industrial y administrativa171 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Industria y OrganizacionesFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaLogística humanitariaDistribución de ayuda humanitariaRestauración del accesoDesastre natural súbitoModelo matemáticoHumanitarian logisticsHumanitarian aid distributionAccess restorationSudden natural disasterMathematical modelAsistencia por desastreInfraestructura de transportesAmenaza naturalDisaster reliefTransport infrastructureNatural hazardsCoordinación de la distribución de ayudas humanitarias con la restauración de disrupciones viales transitorias en zonas afectadas por desastres naturales súbitos con limitaciones de accesibilidadCoordination of the distribution of humanitarian aid with the restoration of transitory road disruptions in areas affected by sudden natural disasters with constraints of accessibilityTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAgarwal, S., Kant, R., & Shankar, R. (2019). Humanitarian supply chain management frameworks: A critical literature review and framework for future development. Benchmarking, 26(6), 1749–1780. https://doi.org/10.1108/BIJ-08-2018-0245Ahmadi, M., Seifi, A., & Tootooni, B. (2015a). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, 75, 145–163. https://doi.org/10.1016/j.tre.2015.01.008Akbari, V., & Salman, F. S. (2017a). Multi-vehicle prize collecting arc routing for connectivity problem. Computers and Operations Research, 82, 52–68. https://doi.org/10.1016/j.cor.2017.01.007Akbari, V., & Salman, F. S. (2017b). Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity. European Journal of Operational Research, 257(2), 625–640. https://doi.org/https://doi.org/10.1016/j.ejor.2016.07.043Akbari, V., Hesam, M., Kiam, R. (2021). A decomposition-based heuristic for a multicrew coordinated road restoration problem.Transportation Research Part D: Transport and Environment, 95, 1-35. https://doi.org/10.1016/j.trd.2021.102854Akbari, V., Hesam, Sayarshad, H. (2022). Integrated and coordinated relief logistics and road recovery planning problem. Transportation Research Part D: Transport and Environment, 111, 1-50. https://doi.org/10.1016/j.trd.2022.103433Akhtar, P., Marr, N. E., & Garnevska, E. V. (2012a). Coordination in humanitarian relief chains: chain coordinators. Journal of Humanitarian Logistics and Supply Chain Management, 2(1), 85–103. https://doi.org/10.1108/20426741211226019Akin, D., & Kara, D. (2020). Multicriteria analysis of planned intercity bus terminals in the metropolitan city of Istanbul, Turkey. Transportation Research Part A: Policy and Practice, 132, 465–489. https://doi.org/https://doi.org/10.1016/j.tra.2019.12.003Aksu, D. T., & Ozdamar, L. (2014a). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E-Logistics and Transportation Review, 61, 56–67. https://doi.org/10.1016/j.tre.2013.10.009Al Theeb, N., & Murray, C. (2017). Vehicle routing and resource distribution in postdisaster humanitarian relief operations. International Transactions in Operational Research, 24(6), 1253–1284. https://doi.org/10.1111/itor.12308Aldunate, R. G., Pena-Mora, F., & Robinson, G. E. (2005). Collaborative distributed decision making for large scale disaster relief operations: Drawing analogies from robust natural systems. Complexity, 11(2), 28–38. https://doi.org/10.1002/cplx.20106Alinaghian, M., Aghaie, M., & Sabbagh, M. S. (2019). A mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles. Computers and Industrial Engineering, 131(17), 227–241. https://doi.org/10.1016/j.cie.2019.03.002Altay, N., Prasad, S., & Tata, J. (2013). A dynamic model for costing disaster mitigation policies. Disasters, 37(3), 357–373. https://doi.org/10.1111/disa.12004Alumur, S. A., Kara, B. Y., & Karasan, O. E. (2012). Multimodal hub location and hub network design. Omega, 40(6), 927–939. https://doi.org/10.1016/j.omega.2012.02.005Anaya-Arenas, A. M., Renaud, J., & Ruiz, A. (2014a). Relief distribution networks: a systematic review. Annals of Operations Research, 223(1), 53–79. https://doi.org/10.1007/s10479-014-1581-yAnjomshoae, A., Hassan, A., & Wong, K. Y. (2019). An integrated AHP-based scheme for performance measurement in humanitarian supply chains. International Journal of Productivity and Performance Management, 68(5), 938–957. https://doi.org/10.1108/IJPPM-04-2018-0132Aranda Pinilla, J. A., & Orjuela Castro, J. A. (2015). Optimización multiobjetivo en la gestión de cadenas de suministro de biocombustibles. Una revisión de la literatura. Ingeniería, 20(1). https://doi.org/10.14483/udistrital.jour.reving.2015.1.a03Arif, A., Wang, Z., Chen, C., & Chen, B. (2020a). A Stochastic Multi-Commodity Logistic Model for Disaster Preparation in Distribution Systems. IEEE Transactions on Smart Grid, 11(1), 565–576. https://doi.org/10.1109/TSG.2019.2925620Aros, S. K., & Gibbons, D. E. (2018). Exploring communication media options in an inter-organizational disaster response coordination network using agent-based simulation. European Journal of Operational Research, 269(2), 451–465. https://doi.org/10.1016/j.ejor.2018.02.013Aslan, E., & Celik, M. (2019). Pre-positioning of relief items under road/facility vulnerability with concurrent restoration and relief transportation. Iise Transactions, 51(8), 847–868. https://doi.org/10.1080/24725854.2018.1540900Averbakh, I. (2012). Emergency path restoration problems. Discrete Optimization, 9(1), 58–64. https://doi.org/10.1016/j.disopt.2012.01.001Baharmand, H., Comes, T., & Lauras, M. (2020). Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters: A case study of the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction, 45(December 2019), 101455. https://doi.org/10.1016/j.ijdrr.2019.101455Bai, X., Gao, J., & Liu, Y. (2018). Prepositioning emergency supplies under uncertainty. parametric optimization method. Engineering Optimization, 50(7), 1114–1133. https://doi.org/10.1080/0305215X.2017.1328508Balcik, B. (2016). Special issue in humanitarian operations. Surveys in Operations Research and Management Science, 21(2), 29–30. https://doi.org/10.1016/j.sorms.2017.02.001Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics Research and Applications, 11(2), 101–121. https://doi.org/10.1080/13675560701561789Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22–34. https://doi.org/10.1016/j.ijpe.2009.09.008Balcik, B., Silvestri, S., Rancourt, M. E., & Laporte, G. (2019). Collaborative Prepositioning Network Design for Regional Disaster Response. Production and Operations Management, 28(10), 2431–2455. https://doi.org/10.1111/poms.13053Banomyong, R., & Sopadang, A. (2010). Using Monte Carlo simulation to refine emergency logistics response models: a case study. International Journal of Physical Distribution & Logistics Management, 40(8–9), 709–721. https://doi.org/10.1108/09600031011079346Barrera, D., Velasco, N., & Amaya, C. A. (2012). A network-based approach to the multi-activity combined timetabling and crew scheduling problem: Workforce scheduling for public health policy implementation. Computers and Industrial Engineering, 63(4), 802–812. https://doi.org/10.1016/j.cie.2012.05.002Baxter, A. E., Wilborn Lagerman, H. E., & Keskinocak, P. (2020). Quantitative modeling in disaster management: A literature review. IBM Journal of Research and Development, 64(1–2). https://doi.org/10.1147/JRD.2019.2960356Baykasoğlu, A., Subulan, K., Taşan, A. S., & Dudaklı, N. (2019). A review of fleet planning problems in single and multimodal transportation systems. Transportmetrica A: Transport Science 15 (2), 631-697.Bealt, J., Fernández Barrera, J. C., & Mansouri, S. A. (2016). Collaborative relationships between logistics service providers and humanitarian organizations during disaster relief operations. Journal of Humanitarian Logistics and Supply Chain Management, 6(2), 118–144. https://doi.org/10.1108/JHLSCM-02-2015-0008Berariu, R., Fikar, C., Gronalt, M., & Hirsch, P. (2015). Understanding the impact of cascade effects of natural disasters on disaster relief operations. International Journal of Disaster Risk Reduction, 12, 350–356. https://doi.org/https://doi.org/10.1016/j.ijdrr.2015.03.005Berumen, S. a, & Llamazares, F. (2007). La utilidad de los métodos de decisión multicriterio (como el AHP) en un entorno de competitividad creciente. Cuadernos de Administración, 20(34), 65–87.Bhamidipati, S. (2015). Simulation Framework for Asset Management in Climate-change Adaptation of Transportation Infrastructure. Transportation Research Procedia, 8, 17–28. https://doi.org/https://doi.org/10.1016/j.trpro.2015.06.038Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485–498. https://doi.org/10.1016/j.ijdrr.2017.01.017Botti, L., Bragatto, P. A., Duraccio, V., Gnoni, M. G., & Mora, C. (2016). Adopting IOT technologies to control risks in confined space: A multi-criteria decision tool. Chemical Engineering Transactions, 53, 127–132. https://doi.org/10.3303/CET1653022Braekers, K., Caris, A., & Janssens, G. K. (2014). Bi-objective optimization of drayage operations in the service area of intermodal terminals. Transportation Research Part E: Logistics and Transportation Review, 65, 50–69. https://doi.org/10.1016/j.tre.2013.12.012Braekers, K., Hartl, R. F., Parragh, S. N., & Tricoire, F. (2016). A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience. European Journal of Operational Research, 248(2), 428–443. https://doi.org/10.1016/j.ejor.2015.07.028Brower, R. S., Choi, S. O., Jeong, H. S., & Dilling, J. (2009). Forms of Inter-Organizational Learning in Emergency Management Networks. Journal of Homeland Security and Emergency Management, 6(1). %3CGoCailloux, O., Mayag, B., Meyer, P., & Mousseau, V. (2013). Operational tools to build a multicriteria territorial risk scale with multiple stakeholders. Reliability Engineering & System Safety, 120, 88–97. https://doi.org/10.1016/j.ress.2013.06.004Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4-13.Çelik, M. (2016). Network restoration and recovery in humanitarian operations: Framework, literature review, and research directions. Surveys in Operations Research and Management Science, 21(2), 47–61. https://doi.org/10.1016/j.sorms.2016.12.001Çelik, M., Ergun, Ö., & Keskinocak, P. (2015). The post-disaster debris clearance problem under incomplete information. Operations Research, 63(1), 65–85. https://doi.org/10.1287/opre.2014.1342Centre for Research on the Epidemiology of Disasters (CRED). (2020). EM-DAT: The Emergency Events.Chang, F.-S., Wu, J.-S., Lee, C.-N., & Shen, H.-C. (2014). Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling. Expert Systems with Applications, 41(6), 2947–2956. https://doi.org/10.1016/j.eswa.2013.10.026Chen, X., Kong, Y., Dang, L., Hou, Y., & Ye, X. (2016). Exact and metaheuristic approaches for a bi-objective school bus scheduling problem. PLoS ONE, 11(4), 1–20. https://doi.org/10.1371/journal.pone.0132600Chen, Y. xin, Tadikamalla, P. R., Shang, J., & Song, Y. (2020). Supply allocation: bi-level programming and differential evolution algorithm for Natural Disaster Relief. Cluster Computing, 23(1), 203–217. https://doi.org/10.1007/s10586-017-1366-6Chriest, A., & Niles, M. (2018). The role of community social capital for food security following an extreme weather event. Journal of Rural Studies, 64, 80–90. https://doi.org/https://doi.org/10.1016/j.jrurstud.2018.09.019Cinelli, M., Coles, S. R., & Kirwan, K. (2014). Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment. Ecological Indicators, 46, 138–148. https://doi.org/10.1016/j.ecolind.2014.06.011Coco, A. A., Duhamel, C., & Santos, A. C. (2020). Modeling and solving the multi-period disruptions scheduling problem on urban networks. Annals of Operations Research, 285(1–2), 427–443. https://doi.org/10.1007/s10479-019-03248-5Coello Coello, C. a, & Lamont, G. B. (2004). An introduction to multi-objective evolutionary algorithms and their applications. In C. A. C. Coello & G. B. Lamont (Eds.), Applications of Multi-Objective Evolutionary Algorithms: Advances in Natural Computation (Vol. 1, Issue 1, p. 792). World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/5712Comité municipal para la gestión de riesgos y desastres de Roldanillo. (2020). Plan Municipal de Gestión del Riesgo - PMGR.Contreras, D., Blaschke, T., Kienberger, S., & Zeil, P. (2014). Myths and realities about the recovery of L׳Aquila after the earthquake. International Journal of Disaster Risk Reduction, 8, 125–142. https://doi.org/https://doi.org/10.1016/j.ijdrr.2014.02.001Cozzolino, A., Rossi, S., & Conforti, A. (2012a). Agile and lean principles in the humanitarian supply chain: The case of the United Nations World Food Programme. Journal of Humanitarian Logistics and Supply Chain Management, 2(1), 16–33. https://doi.org/10.1108/20426741211225984De Keyser, W., & Peeters, P. (1996). A note on the use of PROMETHEE multicriteria methods. European Journal of Operational Research, 89(3), 457–461. https://doi.org/10.1016/0377-2217(94)00307-6Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. In IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (Vol. 6, Issue 2).Delgado-Antequera, L., Caballero, R., Sánchez-Oro, J., Colmenar, J. M., & Martí, R. (2020). Iterated greedy with variable neighborhood search for a multiobjective waste collection problem. Expert Systems with Applications, 145, 113101. https://doi.org/https://doi.org/10.1016/j.eswa.2019.113101Donnini, M., Napolitano, E., Salvati, P., Ardizzone, F., Bucci, F., Fiorucci, F., Santangelo, M., Cardinali, M., & Guzzetti, F. (2017). Impact of event landslides on road networks: a statistical analysis of two Italian case studies. Landslides, 14(4), 1521–1535. https://doi.org/10.1007/s10346-017-0829-4Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2019). Disaster relief operations: past, present and future. Annals of Operations Research, 283, 1–8.Duque, P. A. M., Dolinskaya, I. S., & Sorensen, K. (2016). Network repair crew scheduling and routing for emergency relief distribution problem. European Journal of Operational Research, 248(1), 272–285. https://doi.org/10.1016/j.ejor.2015.06.026Edrissi, A., Nourinejad, M., & Roorda, M. J. (2015). Transportation network reliability in emergency response. Transportation Research Part E: Logistics and Transportation Review, 80, 56–73. https://doi.org/10.1016/j.tre.2015.05.005Edwards, W., & Barron, F. H. (1994). Smarts and smarter: Improved simple methods for multiattribute utility measurement. In Organizational Behavior and Human Decision Processes (Vol. 60, Issue 3, pp. 306–325). https://doi.org/10.1006/obhd.1994.1087Enrique, J., & Herrera, P. (2016). Estrategias de coordinación en operaciones de cargue y descargue de mercancías. Trabajo de grado de maestría. Universidad Nacional de Colombia, Bogotá D.C.Ergu, D., & Kou, G. (2012). Questionnaire design improvement and missing item scores estimation for rapid and efficient decision making. Annals of Operations Research, 197(1), 5–23. https://doi.org/10.1007/s10479-011-0922-3Faiz, T. I., Vogiatzis, C., & Noor-E-Alam, M. (2019). A column generation algorithm for vehicle scheduling and routing problems. Computers and Industrial Engineering, 130(August 2018), 222–236. https://doi.org/10.1016/j.cie.2019.02.032Fazel Zarandi, M. H., Davari, S., & Haddad Sisakht, S. A. (2012). The -coverage multiple allocation hub covering problem with mandatory dispersion. Scientia Iranica, 19(3), 902–911. https://doi.org/10.1016/j.scient.2012.03.007Fikar, C., Gronalt, M., & Hirsch, P. (2016). A decision support system for coordinated disaster relief distribution. Expert Systems with Applications, 57, 104–116. https://doi.org/10.1016/j.eswa.2016.03.039Frachtenberg, E. (2019). Practical Drone Delivery. Computer, 52(12), 53–57. https://doi.org/10.1109/MC.2019.2942290García-Alviz, J., Galindo, G., Arellana, J., & Yie-Pinedo, R. (2021). Planning road network restoration and relief distribution under heterogeneous road disruptions. OR Spectrum, 43(4), 941–981. https://doi.org/10.1007/s00291-021-00644-xGiron, X., & Madrid, D. (2008). Comparación de Dos Métodos de Toma de Decisiones Multicriterio: MACBETH y PROMETHÉE (Spanish Version). Departament d’Enginyeria Informática i Matemátiques.Gossler, T., Wakolbinger, T., & Burkart, C. (2020). Outsourcing in humanitarian logistics – status quo and future directions. International Journal of Physical Distribution and Logistics Management, 50(4), 403–438. https://doi.org/10.1108/IJPDLM-12-2018-0400Gossler, T., Wakolbinger, T., Nagurney, A., & Daniele, P. (2019). How to increase the impact of disaster relief: A study of transportation rates, framework agreements and product distribution. European Journal of Operational Research, 274(1), 126–141. https://doi.org/10.1016/j.ejor.2018.09.045Grass, E., & Fischer, K. (2016). Two-stage stochastic programming in disaster management: A literature survey. Surveys in Operations Research and Management Science, 21(2), 85–100. https://doi.org/10.1016/j.sorms.2016.11.002Grinberger, A. Y., & Felsenstein, D. (2016). Dynamic agent based simulation of welfare effects of urban disasters. Computers Environment and Urban Systems, 59, 129–141. https://doi.org/10.1016/j.compenvurbsys.2016.06.005Groenendaal, J., Helsloot, I., & Scholtens, A. (2013). A Critical Examination of the Assumptions Regarding Centralized Coordination in Large-Scale Emergency Situations. Journal of Homeland Security and Emergency Management, 10(1). https://doi.org/10.1515/jhsem-2012-0053Gschwind, T., Irnich, S., Tilk, C., & Emde, S. (2019). Branch-cut-and-price for scheduling deliveries with time windows in a direct shipping network. Journal of Scheduling. https://doi.org/10.1007/s10951-019-00620-yGuan, G., Lin, Z., Gong, Y., & Jiang, Z. (2020). Modeling and Simulation of Collaborative Dispatching of Disaster Relief Materials Based on Urgency. Mathematical Problems in Engineering, 2020, 1DUMMMY. https://doi.org/10.1155/2020/4274106Guo, L., Li, Y. P., Huang, G. H., Wang, X. W., & Dai, C. (2012). Development of an Interval-Based Evacuation Management Model in Response to Nuclear-Power Plant Accident. Journal of Environmental Informatics, 20(2), 58–66. https://doi.org/10.3808/jei.201200220Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351–366. https://doi.org/10.1016/j.ejor.2015.12.035Guzmán Cortés, D. C., & Guerrero, W. J. (2018). Inventory routing models for humanitarian relief. In The 9th International Conference on Production Research – Americas 2018 (Vol. 1).Habib, M. S., Lee, Y. H., & Memon, M. S. (2016). Mathematical Models in Humanitarian Supply Chain Management: A Systematic Literature Review. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/3212095Hammontree, E., Barrows, R., & Allen, B. (2000). Western federal lands highway division responds to northwest emergencies.Hasani, A., & Mokhtari, H. (2019). An integrated relief network design model under uncertainty: A case of Iran. Safety Science, 111, 22–36. https://doi.org/10.1016/j.ssci.2018.09.004Helderop, E., & Grubesic, T. H. (2019). Flood evacuation and rescue: The identification of critical road segments using whole-landscape features. Transportation Research Interdisciplinary Perspectives, 3, 100022. https://doi.org/10.1016/j.trip.2019.100022Henao, D., López, F., Chud Pantoja, V. L., & Osorio, J. C. (2019). Priorización multicriterio para la afiliación a un banco de alimentos en Colombia. Revista Logos, Ciencia & Tecnología, 12(1), 58–70. https://doi.org/10.22335/rlct.v12i1.1024Holguin-Veras, J., Jaller, M., Van Wassenhove, L. N., Perez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of Operations Management, 30(7–8), 494–506. https://doi.org/10.1016/j.jom.2012.08.003Hosseini, S. M. A., de la Fuente, A., & Pons, O. (2016). Multicriteria Decision-Making Method for Sustainable Site Location of Post-Disaster Temporary Housing in Urban Areas. Journal of Construction Engineering and Management, 142(9). https://doi.org/10.1061/(asce)co.1943-7862.0001137Hu, F.-H., Feng, X., Zhao, M.-H., & Chen, Y.-R. (2015). Research on emergency supplies dispatching problem based on genetic algorithm. Open Cybernetics and Systemics Journal, 9(1), 62–68.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 (English Edition), 6(5), 441–454. https://doi.org/10.1016/j.jtte.2019.07.001Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Transportation Research Part E: Logistics and Transportation Review, 48(1), 2–18. https://doi.org/10.1016/j.tre.2011.05.004Huizing, D., Schäfer, G., van der Mei, R. D., & Bhulai, S. (2020). The median routing problem for simultaneous planning of emergency response and non-emergency jobs. European Journal of Operational Research.Hwang, G., Han, J. H., & Chang, T. W. (2020). An integrated key performance measurement for manufacturing operations management. Sustainability (Switzerland), 12(13), 1–15. https://doi.org/10.3390/su12135260Ibarra-Rojas, O. J., Hernandez, L., & Ozuna, L. (2018). The Accessibility Vehicle Routing Problem. Journal of Cleaner Production, 172, 1514–1528. https://doi.org/10.1016/j.jclepro.2017.10.249Ibn Faiz, T., Vogiatzis, C., & Noor-E-Alam, M. (2019). A column generation algorithm for vehicle scheduling and routing problems. Computers & Industrial Engineering, 130, 222–236. https://doi.org/10.1016/j.cie.2019.02.032Iloglu, S., & Albert, L. A. (2018). An integrated network design and scheduling problem for network recovery and emergency response. Operations Research Perspectives, 5(August), 218–231. https://doi.org/10.1016/j.orp.2018.08.001Iloglu, S., & Albert, L. A. (2020). A maximal multiple coverage and network restoration problem for disaster recovery. Operations Research Perspectives.Immawan, T., Pratiwi, A. I., & Cahyo, W. N. (2019). The proposed dashboard model for measuring performance of Small-Medium Enterprises (SME). International Journal of Integrated Engineering, 11(5), 167–173. https://doi.org/10.30880/ijie.2019.11.05.021Instituto Nacional de Vías (INVIAS) (2016). Manual de mantenimiento de carreteras. URL: https://www.invias.gov.co/index.php/archivo-y-documentos/documentos-tecnicos/7714-manual-de-mantenimiento-de-carreteras-2016-v2Jahre, M., & Jensen, L. M. (2010). Coordination in humanitarian logistics through clusters. International Journal of Physical Distribution and Logistics Management, 40(8), 657–674. https://doi.org/10.1108/09600031011079319Janiak, M. K., & Zak, J. (2014). Multiple criteria evaluation of different redesign variants of the public tram system. Transportation Research Procedia, 3(July), 690–699. https://doi.org/10.1016/j.trpro.2014.10.048Jiang, J. P., Wang, P., Lung, W. S., Guo, L., & Li, M. (2012). A GIS-based generic real-time risk assessment framework and decision tools for chemical spills in the river basin. Journal of Hazardous Materials, 227, 280–291. https://doi.org/10.1016/j.jhazmat.2012.05.051Jiang, Y. P., & Yuan, Y. F. (2019). Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges. International Journal of Environmental Research and Public Health, 16(5). https://doi.org/10.3390/ijerph16050779Kalinowski, T., Matsypura, D., & Savelsbergh, M. W. P. (2015). Incremental network design with maximum flows. European Journal of Operational Research, 242(1), 51–62. https://doi.org/10.1016/j.ejor.2014.10.003Kalkman, J. P., & de Waard, E. J. (2017). Inter-organizational disaster management projects: Finding the middle way between trust and control. International Journal of Project Management, 35(5), 889–899. https://doi.org/10.1016/j.ijproman.2016.09.013Karakoc, D. B., Almoghathawi, Y., Barker, K., González, A. D., & Mohebbi, S. (2019). Community resilience-driven restoration model for interdependent infrastructure networks. International Journal of Disaster Risk Reduction, 38(June), 101228. https://doi.org/10.1016/j.ijdrr.2019.101228Karakoc, D. B., Barker, K., Zobel, C. W., & Almoghathawi, Y. (2020). Social vulnerability and equity perspectives on interdependent infrastructure network component importance. Sustainable Cities and Society, 57(March 2019), 102072. https://doi.org/10.1016/j.scs.2020.102072Khademi, N., Balaei, B., Shahri, M., Mirzaei, M., Sarrafi, B., Zahabiun, M., & Mohaymany, A. S. (2015). Transportation network vulnerability analysis for the case of a catastrophic earthquake. International Journal of Disaster Risk Reduction, 12, 234–254. https://doi.org/https://doi.org/10.1016/j.ijdrr.2015.01.009Khan, M. U., Mesbah, M., Ferreira, L., & Williams, D. J. (2014). Development of road deterioration models incorporating flooding for optimum maintenance and rehabilitation strategies. Road & Transport Research, 23(1), 3–24.Kim, S., Shin, Y., Lee, G. M., & Moon, I. (2018). Network repair crew scheduling for short-term disasters. Applied Mathematical Modelling, 64, 510–523. https://doi.org/10.1016/j.apm.2018.07.047Kovacs, G., & Moshtari, M. (2019). A roadmap for higher research quality in humanitarian operations: A methodological perspective. European Journal of Operational Research, 276(2), 395–408. https://doi.org/10.1016/j.ejor.2018.07.052Kovacs, G., & Spens, K. (2009). Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management, 39(6), 506–528. https://doi.org/10.1108/09600030910985848Kumar, B., & Bhaduri, S. (2018). Disaster risk in the urban villages of Delhi. International Journal of Disaster Risk Reduction, 31, 1309–1325. https://doi.org/https://doi.org/10.1016/j.ijdrr.2018.04.022Laguna-Salvado, L., Lauras, M., Okongwu, U., & Comes, T. (2019). A multicriteria Master Planning DSS for a sustainable humanitarian supply chain. Annals of Operations Research, 283(1–2), 1303–1343. https://doi.org/10.1007/s10479-018-2882-3Lamos Díaz, H., Aguilar Imitola, K., & Acosta Amado, R. J. (2019). Perspectivas de investigación en OR/MS en la gestión de operaciones de desastres: una revisión de la literatura. Revista Facultad De Ingeniería Universidad De Antioquia, (91), 43–59. https://doi.org/10.17533/udea.redin.n91a05Larichev, O. I., & Moshkovich, H. M. (1995a). ZAPROS-LM - A method and system for ordering multiattribute alternatives. European Journal of Operational Research, 82(3), 503–521. https://doi.org/10.1016/0377-2217(93)E0143-LLeiras, A., de Brito Jr., I., Queiroz Peres, E., Rejane Bertazzo, T., & Yoshida Yoshizaki, H. (2014). Literature review of humanitarian logistics research: Trends and challenges. Journal of Humanitarian Logistics and Supply Chain Management, 4(1), 95-130. Disponible en: https://doi.org/10.1108/JHLSCM-04-2012-0008Li, M. Y., Zhao, X. J., Fan, Z. P., Cao, P. P., & Qu, X. N. (2019). A model for assignment of rescuers considering multiple disaster areas. International Journal of Disaster Risk Reduction, 38(June), 101201. https://doi.org/10.1016/j.ijdrr.2019.101201Li, P., Lan, H., & Saldanha-Da-Gama, F. (2019). A Bi-objective capacitated location-routing problem for multiple perishable commodities. IEEE Access.Lin, Y.-H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2011). A logistics model for emergency supply of critical items in the aftermath of a disaster. Socio-Economic Planning Sciences, 45(4), 132–145. https://doi.org/https://doi.org/10.1016/j.seps.2011.04.003Liu, B., Sheu, J.-B., Zhao, X., Chen, Y., & Zhang, W. (2020). Decision Making on Post-Disaster Rescue Routing Problems from the Rescue Efficiency Perspective. European Journal of Operational Research. https://doi.org/https://doi.org/10.1016/j.ejor.2020.03.017Liu, C., Kou, G., Peng, Y., & Alsaadi, F. E. (2019). Location-routing problem for relief distribution in the early post-earthquake stage from the perspective of fairness. Sustainability (Switzerland), 11(12). https://doi.org/10.3390/SU11123420Liu, M., & Liang, J. (2013). Dynamic optimization model for allocating medical resources in epidemic controlling. Journal of Industrial Engineering and Management, 6(1), 73-88. http://dx.doi.org/10.3926/jiem.663Liu, M., & Xiao, Y. (2015). Optimal scheduling of logistical support for medical resource with demand information updating. Mathematical Problems in Engineering, 2015. https://doi.org/10.1155/2015/765098Liu, M., & Zhao, L. (2012). An integrated and dynamic optimisation model for the multi-level emergency logistics network in anti-bioterrorism system. International Journal of Systems Science, 43(8), 1464–1478. https://doi.org/10.1080/00207721.2010.547629Liubartseva, S., Coppini, G., Pinardi, N., De Dominicis, M., Lecci, R., Turrisi, G., Cret, S., Martinelli, S., Agostini, P., Marra, P., & Palermo, F. (2016). Decision support system for emergency management of oil spill accidents in the Mediterranean Sea. Natural Hazards and Earth System Sciences, 16(8), 2009–2020. https://doi.org/10.5194/nhess-16-2009-2016López-Ramírez, M. A., Rojas-Trejos, C. A., González-Velasco, J., Valle, U., & Industrial, E. D. I. (2017). Inventory control model using the Two - Bin System methodology in the hospital network of the City of Cali, Colombia. International Journal of Combinatorial Optimization Problems and Informatics, 8(2), 19–24.López-Vargas, J. C., & Cárdenas-Aguirre, D. M. (2018). Factores de influencia en la coordinación logística para la preparación y atención de desastres – Una revisión de literatura. Revista EIA, 15(30), 41–56. https://doi.org/10.24050/reia.v15i30.1146Lorca, A., Celik, M., Ergun, O., & Keskinocak, P. (2017). An Optimization-Based Decision-Support Tool for Post-Disaster Debris Operations. Production and Operations Management, 26(6), 1076–1091. https://doi.org/10.1111/poms.12643Loree, N., & Aros-Vera, F. (2018). Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics. Transportation Research Part E-Logistics and Transportation Review, 116, 1–24. https://doi.org/10.1016/j.tre.2018.05.003Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R., Ahn, S. Y., AlMazroa, M. A., Alvarado, M., Anderson, H. R., Anderson, L. M., Andrews, K. G., Atkinson, C., Baddour, L. M., Barker-Collo, S., Bartels, D. H., … Murray, C. J. L. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2095–2128. https://doi.org/10.1016/S0140-6736(12)61728-0Lu, G., Xiong, Y., Ding, C., & Wang, Y. (2016a). An optimal schedule for urban road network repair based on the greedy algorithm. PLoS ONE.Macea, L. F., Amaya, J., Cantillo, V., & Holguín-Veras, J. (2018). Evaluating economic impacts of water deprivation in humanitarian relief distribution using stated choice experiments. International Journal of Disaster Risk Reduction, 28(July 2017), 427–438. https://doi.org/10.1016/j.ijdrr.2018.03.029Mahecha Peláez L.A., & Rojas Trejos, C. A. (2019). Propuesta de un modelo de localización de albergues temporales para la atención de damnificados en la fase de respuesta post desastre: caso inundación Roldanillo Valle Trabajo de grado para optar el título de Ingeniero Industrial - Universidad del Valle. Sede Zarzal.Maghsoudi, A., & Pazirandeh, A. (2016). Visibility, resource sharing and performance in supply chain relationships: insights from humanitarian practitioners. Supply Chain Management, 21(1), 125–139. https://doi.org/10.1108/SCM-03-2015-0102Malone, T. W. (1987). Modeling Coordination in Organizations and Markets. Management Science, 33(10), 1317–1332. https://doi.org/10.1287/mnsc.33.10.1317Manopiniwes, W., & Irohara, T. (2014). A review of relief supply chain optimization. Industrial Engineering and Management Systems, 13(1), 1-14.Mansoori, S., Bozorgi-Amiri, A., & Pishvaee, M. S. (2020). A robust multi-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties. Neural Computing and Applications.Mardani, A., Zavadskas, E. K., Govindan, K., Senin, A. A., & Jusoh, A. (2016a). VIKOR technique: A systematic review of the state of the art literature on methodologies and applications. Sustainability (Switzerland), 8(1), 1–38. https://doi.org/10.3390/su8010037Marín Gómez, K. V., Morales Corredor J.D, & Rojas Trejos, C. A. (2018). Propuesta de un modelo de localización de albergues temporales y ruteo de personal especializado, para la atención de una población vulnerable ante un desastre de la región. Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal.Martínez López V.M., & Rojas Trejos, C. A. (2020). Propuesta de distribución de ayuda humanitaria considerando restricciones viales en desastres naturales súbitos. Caso de estudio: Roldanillo - Valle del Cauca Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal.Matsumaru, R., Nagami, K., & Takeya, K. (2012). Reconstruction of the Aceh Region following the 2004 Indian Ocean tsunami disaster: A transportation perspective. IATSS Research, 36(1), 11–19. https://doi.org/https://doi.org/10.1016/j.iatssr.2012.07.001Maya Duque, P. A., Coene, S., Goos, P., Sörensen, K., & Spieksma, F. (2013). The accessibility arc upgrading problem. European Journal of Operational Research, 224(3), 458–465. https://doi.org/10.1016/j.ejor.2012.09.005Maya Duque, P. A., Dolinskaya, I. S., & Sörensen, K. (2016). Network repair crew scheduling and routing for emergency relief distribution problem. European Journal of Operational Research, 248(1), 272–285. https://doi.org/10.1016/j.ejor.2015.06.026Mendoza Patiño, D., Betancourt Cortez, F. J., Leguizamón Sierra, G., Dajles Lenis, D. E., Gámez Araújo, W. G., & Rojas Trejos, C. A. (2018a). Determination of the relevant factors in the outsourcing decision-making process in global supply chains applying the AHP methodology. Espacios, 39(41).Messa Mejía E., Sánchez Murilo T., Rojas Trejos, C. A., & González Velasco J. (2022) Propuesta de un modelo de distribución de vacunas considerando limitaciones de inventario en depósitos y puntos de entrega en el Valle del Cauca. Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal.Minas, J. P., Simpson, N. C., & Tacheva, Z. Y. (2020). Modeling emergency response operations: A theory building survey. Computers and Operations Research, 119, 104921. https://doi.org/10.1016/j.cor.2020.104921Ministerio de Ambiente y Desarrollo Sostenible. (2012). Ley 1523: Sistema Nacional de Gestión del Riesgo. Instituto Distrital de Gestión Del Riesgo - IDIGER, 58.Manterola, C., Astudillo, P., Arias, E., & Claros, N. (2013). Systematic reviews of the literature: what should be known about them. Cirugía Española, 149-155.Mohammad, A., Rezapour, S, Baghaian, A. (2023). An integrative framework for coordination of damage assessment, road restoration, and relief distribution in disasters. Omega-International Journal of Management Science. https://doi.org/115 10.1016/j.omega.2022.102748Montoya Calleja V., Ruiz Aguirre M.A., Guerra Ospina P.A., & Rojas Trejos, C. A. (2022, 5 de septiembre). Propuesta de un modelo de programación matemática para la localización de albergues temporales ante eventos de desastre. Caso de estudio: Roldanillo, Valle del Cauca, Colombia. Congreso Asociación Colombiana de Investigación Operativa - Institute of Industrial and Systems Engineers (ASOCIO-IISE) [Ponencia]. Universidad de La Sabana, Universidad Sergio Arboleda, Universidad de los Andes. Bogotá D.C, Colombia.Moreno, A., Alem, D., Gendreau, M., & Munari, P. (2020). The heterogeneous multicrew scheduling and routing problem in road restoration. Transportation Research Part B: Methodological, 141, 24–58. https://doi.org/10.1016/j.trb.2020.09.002Moreno, A., Munari, P., & Alem, D. (2019). A branch-and-Benders-cut algorithm for the Crew Scheduling and Routing Problem in road restoration. European Journal of Operational Research, 275(1), 16–34. https://doi.org/10.1016/j.ejor.2018.11.004Morshedlou, N., González, A. D., & Barker, K. (2018). Work crew routing problem for infrastructure network restoration. Transportation Research Part B: Methodological, 118, 66–89. https://doi.org/10.1016/j.trb.2018.10.001Mostajabdaveh, M., Gutjahr, W. J., & Salman, F. S. (2019). Inequity-averse shelter location for disaster preparedness. Iise Transactions, 51(8), 809–829. https://doi.org/10.1080/24725854.2018.1496372Mutebi, H., Muhwezi, M., Ntayi, J. M., & Munene, J. C. K. (2020). Organisation size, innovativeness, self-organisation and inter-organisational coordination. International Journal of Emergency Services, 9(3), 359–394. https://doi.org/10.1108/IJES-05-2020-0024Nassereddine, M., Azar, A., Rajabzadeh, A., & Afsar, A. (2019). Decision making application in collaborative emergency response: A new PROMETHEE preference function. International Journal of Disaster Risk Reduction, 38. https://doi.org/10.1016/j.ijdrr.2019.101221Nurmala, N., de Leeuw, S., & Dullaert, W. (2017). Humanitarian–business partnerships in managing humanitarian logistics. Supply Chain Management, 22(1), 82–94. https://doi.org/10.1108/SCM-07-2016-0262Nurre, S. G., Cavdaroglu, B., Mitchell, J. E., Sharkey, T. C., & Wallace, W. A. (2012a). Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem. European Journal of Operational Research, 223(3), 794–806. https://doi.org/10.1016/j.ejor.2012.07.010Osorio, A., Brailsford, S. & Smith, H. (2014). Un modelo de optimización bi-objetivo para la selección de tecnología y asignación de donantes en la cadena de suministro de sangre. Revista S&T, 12(30), 9-24Osorio, C.A. (2016). Mecanismos de coordinación para la optimización del desempeño de la cadena logística humanitaria mediante modelamiento estocástico. Caso colombiano. Tesis de doctorado. Universidad Nacional de Colombia, Bogotá D.C.Ozdamar, L., Aksu, D. T., Yasa, E., & Ergunes, B. (2018). Disaster relief routing in limited capacity road networks with heterogeneous flows. Journal of Industrial and Management Optimization.Özdamar, L., & Ertem, M. A. (2015). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55–65. https://doi.org/10.1016/j.ejor.2014.11.030Özdamar, L., Tüzün Aksu, D., & Ergüneş, B. (2014). Coordinating debris cleanup operations in post disaster road networks. Socio-Economic Planning Sciences, 48(4), 249–262. https://doi.org/10.1016/j.seps.2014.08.001Papamichail, K. N., & French, S. (2013). 25 Years of MCDA in nuclear emergency management. Ima Journal of Management Mathematics, 24(4), 481–503. https://doi.org/10.1093/imaman/dps028Paraskevopoulos, D. C., Laporte, G., Repoussis, P. P., & Tarantilis, C. D. (2017). Resource constrained routing and scheduling: Review and research prospects. European Journal of Operational Research.Paul, J. A., & MacDonald, L. (2016). Optimal location, capacity and timing of stockpiles for improved hurricane preparedness. International Journal of Production Economics, 174, 11–28. https://doi.org/10.1016/j.ijpe.2016.01.006Perez-Rodriguez, N., & Holguin-Veras, J. (2016). Inventory-Allocation Distribution Models for Postdisaster Humanitarian Logistics with Explicit Consideration of Deprivation Costs. Transportation Science, 50(4), 1261–1285. https://doi.org/10.1287/trsc.2014.0565Podgórski, D. (2015). Measuring operational performance of OSH management system - A demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146–166. https://doi.org/10.1016/j.ssci.2014.11.018Prabawa, P., & Choi, D.-H. (2020). Multi-Agent Framework for Service Restoration in Distribution Systems with Distributed Generators and Static/Mobile Energy Storage Systems. IEEE Access, 8, 51736–51752. https://doi.org/10.1109/ACCESS.2020.2980544Pradhananga, R., Mutlu, F., Pokharel, S., Holguin-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.2015.11.010Prakash, C., Besiou, M., Charan, P., & Gupta, S. (2020). Organization theory in humanitarian operations: a review and suggested research agenda. Journal of Humanitarian Logistics and Supply Chain Management, 10(2), 261–284. https://doi.org/10.1108/JHLSCM-08-2019-0051Rais, A., Alvelos, F., & Carvalho, M. S. (2014). New mixed integer-programming model for the pickup-and-delivery problem with transshipment. European Journal of Operational Research, 235(3), 530–539. https://doi.org/https://doi.org/10.1016/j.ejor.2013.10.038Ramírez Drada, E., Chud Pantoja, V. L., & Orejuela Cabrera, J. P. (2019). Propuesta metodológica multicriterio para la distribución semicontinua de plantas. Suma de Negocios, 10(23), 132–145. https://doi.org/10.14349/sumneg/2019.v10.n23.a6Ransikarbum, K., & Mason, S. J. (2016). Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration. International Journal of Production Economics, 182, 324–341. https://doi.org/10.1016/j.ijpe.2016.08.030Reddy, G. H., Chakrapani, P., Goswami, A. K., & Choudhury, N. B. D. (2017a). Fuzzy Based Approach for Restoration of Distribution System during Post Natural Disasters. IEEE Access, 6, 3448–3458. https://doi.org/10.1109/ACCESS.2017.2779823Regis-Hernandez, F., Mora-Vargas, J., & Ruiz, A. (2017). A Multi-Criteria Vertical Coordination Framework for a Reliable Aid Distribution. Journal of Industrial Engineering and Management-Jiem, 10(4), 789–815. https://doi.org/10.3926/jiem.2253Resolución No. 1400 (8 de junio de 2004). Ministerio de Transporte. Colombia.Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Cheikhrouhou, N., & Taheri-Moghaddam, A. (2016a). An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management. Transportation Research Part E-Logistics and Transportation Review, 93, 485–509. https://doi.org/10.1016/j.tre.2016.07.003Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Bozorgi-Amiri, A. (2016b). An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Computers and Industrial Engineering, 94, 201–215. https://doi.org/10.1016/j.cie.2016.01.014Ríos, R. C. G., Sánchez, C. N. G., & González, E. L. R. (2019). Application of the scoring method for integral customer classification. Ingeniare, 27(3), 375–382. https://doi.org/10.4067/S0718-33052019000300375Rivera, J. G. (2010). La comunicación del riesgo: hacia un modelo efectivo y situacional (ITM, Ed.). ITM.Rodriguez-Espindola, Oscar, Albores, P., & Brewster, C. (2018). Dynamic formulation for humanitarian response operations incorporating multiple organisations. International Journal of Production Economics, 204(August), 83–98. https://doi.org/10.1016/j.ijpe.2018.07.023Rodríguez Taborda J.D., Gómez Hernández J.M., & Rojas Trejos, C. A. (2022). Propuesta de un modelo de programación de recursos de reparación para disrupciones viales transitorias por desastres naturales súbitos. Trabajo de grado para optar el título de Ingeniero Industrial. Universidad del Valle. Sede Zarzal.Rojas Trejos, C. A., & González Velasco, J. (2018). A Multicriteria Location Model for a Solid Waste Disposal Center in Valle Del Cauca, Colombia. Supply Chain Management and Logistics in Latin America, 37–53. https://doi.org/10.1108/978-1-78756-803-720181004Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2023a). Humanitarian aid distribution logistics with accessibility constraints: a systematic literature review. In Journal of Humanitarian Logistics and Supply Chain Management (Vol. 13, Issue 1, pp. 26–41). Emerald Publishing. https://doi.org/10.1108/JHLSCM-05-2021-0041Rojas Trejos, C. A., Meisel, J. D., Adarme Jaimes, W., & Orejuela Cabrera J. P. (2023b, 30 de marzo). Distribución de ayuda humanitaria considerando disrupciones viales transitorias [Conferencia]. XXVI – II Seminario de Logística. Grupo de Investigación, Sociedad, Economía y Productividad (SEPRO). Universidad Nacional de Colombia. Bogotá D.C, Colombia.Rojas Trejos, C. A., Meisel, J. D., Adarme Jaimes, W., & Orejuela Cabrera J. P. (2022a, 22 de noviembre). Programación de recursos de reparación para la atención de disrupciones viales transitorias en logística humanitaria [Conferencia]. XXV - IV Seminario de Logística. Grupo de Investigación, Sociedad, Economía y Productividad (SEPRO). Universidad Nacional de Colombia. Bogotá D.C, Colombia.Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2021, 22 de agosto). Assignment, location and routing in humanitarian logistics [Ponencia]. 22nd Conference of the International Federation of Operational Research Societies (IFORS). Seúl, Corea del Sur.Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2022b). Enfoque integrado multicriterio para la medición del desempeño en redes de distribución de ayuda humanitaria [Ponencia]. IX Congreso Internacional de Industria y Organizaciones. Universidad Nacional de Colombia. Bogotá D.C, Colombia.Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2023c). X Congreso Internacional de Industria y Organizaciones. Universidad de la Costa, Universidad Nacional de Colombia [Ponencia]. X Congreso Internacional de Industria y Organizaciones. Universidad Nacional de Colombia. Barranquilla, Colombia.Rojas Trejos, C. A., Saavedra Arango, L. F., & Orejuela Cabrera, J. P. (2017). Scheduling of internal materials supply operations in a pre-milling type concentrate food plant. Producción + Limpia, 12(2), 147–158. https://doi.org/10.22507/pml.v12n2a12Rojas-trejos, C. A., González-velasco, J., López-ramírez, M. A., Valle, U., & Industrial, E. D. I. (2017). Optimization Model for the Location of Prehospital Care Ambulances in the city of Cali Colombia. International Journal of Combinatorial Optimization Problems and Informatics, 8(3), 64–70.Roozbahani, A., Zahraie, B., & Tabesh, M. (2012a). PROMETHEE with Precedence Order in the Criteria (PPOC) as a New Group Decision Making Aid: An Application in Urban Water Supply Management. Water Resources Management, 26(12), 3581–3599. https://doi.org/10.1007/s11269-012-0091-4Saaty, R. W. (1987). The analytic hierarchy process-what it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8Sabbaghtorkan, M., Batta, R., & He, Q. (2020). Prepositioning of assets and supplies in disaster operations management: Review and research gap identification. European Journal of Operational Research, 284(1), 1–19. https://doi.org/10.1016/j.ejor.2019.06.029Sabouhi, F., Bozorgi-Amiri, A., & Vaez, P. (2020). Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty. Kybernetes, 50(9), 2632–2650. https://doi.org/10.1108/K-10-2020-0632Safarpour, H., Fooladlou, S., Safi-Keykaleh, M., Mousavipour, S., Pirani, D., Sahebi, A., Ghodsi, H., Farahi-Ashtiani, I., & Dehghani, A. (2020). Challenges and barriers of humanitarian aid management in 2017 Kermanshah earthquake: A qualitative study. BMC Public Health, 20(1), 1–10. https://doi.org/10.1186/s12889-020-08722-5Sahin, H., Kara, B. Y., & Karasan, O. E. (2016). Debris removal during disaster response: A case for Turkey. Socio-Economic Planning Sciences, 53, 49–59. https://doi.org/10.1016/j.seps.2015.10.003Sakiani, R., Seifi, A., & Khorshiddoust, R. R. (2020). Inventory routing and dynamic redistribution of relief goods in post-disaster operations. Computers and Industrial Engineering, 140(December 2019), 106219. https://doi.org/10.1016/j.cie.2019.106219Sakuraba, C. S., Santos, A. C., & Prins, C. (2016a). Work-troop scheduling for road network accessibility after a major earthquake. Electronic Notes in Discrete Mathematics.Sakuraba, C. S., Santos, A. C., Prins, C., Bouillot, L., Durand, A., & Allenbach, B. (2016b). Road network emergency accessibility planning after a major earthquake. EURO Journal on Computational Optimization.Sánchez Pedraza, R., & Jaramillo González, L. (2009). Methodology of Qualification and Summary of the Opinions in Formal Consensus. Revista Colombiana de Psiquiatría, 38(4), 777–786.Sanci, E., & Daskin, M. S. (2019). Integrating location and network restoration decisions in relief networks under uncertainty. European Journal of Operational Research, 279(2), 335–350. https://doi.org/10.1016/j.ejor.2019.06.012Sarache, W. A., Cárdenas, D. M., & Giraldo, J. A. (2005). Procedimiento para la definición y jerarquización de prioridades competitivas de fabricación. Aplicaciones en las pymes de la industria metalmecánica. Ingeniería y Competitivad, 7(2), 84–91.Sarkissian, R. Der, Zaninetti, J.-M., & Abdallah, C. (2019). The use of geospatial information as support for Disaster Risk Reduction; contextualization to Baalbek-Hermel Governorate/Lebanon. Applied Geography, 111, 102075. https://doi.org/https://doi.org/10.1016/j.apgeog.2019.102075Sc, A. M., Ramos, F., & Auxiliar, P. (2011). Una aproximación a la toma de decisiones en el escenario azucarero mediante el método Delphi . Un estudio de caso . An approximation to the take of decision at the sugar scenario , using the method Delphi . A case study . Revista centro azúcar, 38(3), 39–45.Shanshan, H., Wenhai, Z., & Chaofeng, W. (2015). Collaborative dynamic optimization on post-earthquake emergency supply and road rehabilitation based on road network connectivity. Open Automation and Control Systems Journal.Sharma, A., Singh, P. K., Sharma, A., & Kumar, R. (2019). An efficient architecture for the accurate detection and monitoring of an event through the sky. Computer Communications, 148, 115–128. https://doi.org/https://doi.org/10.1016/j.comcom.2019.09.009Shendarkar, A., Vasudevan, K., Lee, S., & Son, Y. J. (2008). Crowd simulation for emergency response using BDI agents based on immersive virtual reality. Simulation Modelling Practice and Theory, 16(9), 1415–1429. https://doi.org/10.1016/j.simpat.2008.07.004Shin, Y., Kim, S., & Moon, I. (2019). Integrated optimal scheduling of repair crew and relief vehicle after disaster. Computers and Operations Research, 105, 237–247. https://doi.org/10.1016/j.cor.2019.01.015Silva, L. de O., Bandeira, R. A. de M., & Campos, V. B. G. (2019a). Proposal to planning facility location using UAV and geographic information systems in a post-disaster scenario. International Journal of Disaster Risk Reduction, 36(December 2018), 101080. https://doi.org/10.1016/j.ijdrr.2019.101080Sirikijpanichkul, A., Van Dam, Koen H., Ferreira, L., & Lukszo, Z. (2007). Optimizing the Location of Intermodal Freight Hubs: An Overview of the Agent Based Modelling Approach. Journal of Transportation Systems Engineering and Information Technology, 7(4), 71–81. https://doi.org/10.1016/S1570-6672(07)60031-2Solmaz, G., & Turgut, D. (2017). Modeling pedestrian mobility in disaster areas. Pervasive and Mobile Computing, 40, 104–122. https://doi.org/https://doi.org/10.1016/j.pmcj.2017.05.005Solomatin, A. (2018). The World Bank and Fragile States: Dynamics of cooperation and aid structure. International Organisations Research Journal, 13(1), 114–132. https://doi.org/10.17323/1996-7845-2018-01-07Sörensen, K., & Vanovermeire, C. (2013). Bi-objective optimization of the intermodal terminal location problem as a policy-support tool. Computers in Industry, 64(2), 128–135. https://doi.org/10.1016/j.compind.2012.10.012Steenbergen, R., Mes, M., Heeswijk, W. (2023). Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty. Transportation Research Part C: Emerging Technologies, 157, 1–28. https://doi.org/10.1016/j.trc.2023.104401Tamima, U., & Chouinard, L. (2016). Development of evacuation models for moderate seismic zones: A case study of Montreal. International Journal of Disaster Risk Reduction, 16, 167–179. https://doi.org/https://doi.org/10.1016/j.ijdrr.2016.02.003Tan, Y., Qiu, F., Das, A. K., Kirschen, D. S., Arabshahi, P., & Wang, J. (2019). Scheduling Post-Disaster Repairs in Electricity Distribution Networks. IEEE Transactions on Power Systems.Tatham, P., & Spens, K. (2011). Towards a humanitarian logistics knowledge management system. Disaster Prevention and Management, 20(1), 6–26. https://doi.org/10.1108/09653561111111054Tatiana, F., & Duarte, M. (2017). Mecanismo de coordinación entre actores públicos y privados del sistema de transporte de carga de Colombia. Tesis de maestría. Universidad Nacional de Colombia, Bogotá D.C.Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations. Socio-Economic Planning Sciences, 64(October 2017), 21–37. https://doi.org/10.1016/j.seps.2017.12.004Turgut, B. T., Tas, G., Herekoglu, A., Tozan, H., & Vayvay, O. (2011). A fuzzy AHP based decision support system for disaster center location selection and a case study for Istanbul. Disaster Prevention and Management, 20(5), 499–520. https://doi.org/10.1108/09653561111178943Tuzun Aksu, D., & Ozdamar, L. (2014). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E: Logistics and Transportation Review, 61, 56–67. https://doi.org/10.1016/j.tre.2013.10.009Tzeng, G. H., Cheng, H. J., & Huang, T. D. (2007). Mufti-objective optimal planning for designing relief delivery systems. Transportation Research Part E-Logistics and Transportation Review, 43(6), 673–686. https://doi.org/10.1016/j.tre.2006.10.012Unidad Nacional para la Gestión de Riesgos de Desastres, U. (2013). Estandarización de ayuda humanitaria.Unidad Nacional para la Gestión del Riesgo de Desastres. (2020). Consolidado Atención de Emergencias. http://portal.gestiondelriesgo.gov.co/Paginas/Consolidado-Atencion-de-Emergencias.aspxUnidad Nacional para la Gestión del Riesgo de Desastres. (2021). Consolidado Atención de Emergencias. http://portal.gestiondelriesgo.gov.co/Paginas/Consolidado-Atencion-de-Emergencias.aspxUpayokin, A., Mattingly, S. P., & Lugo-Serrato, S. A. (2008). Decision-making procedure for assessing performance measures of freeway operations. Transportation Research Record, 2046, 11–19. https://doi.org/10.3141/2046-02Usón, T. J., Klonner, C., & Höfle, B. (2016). Using participatory geographic approaches for urban flood risk in Santiago de Chile: Insights from a governance analysis. Environmental Science & Policy, 66, 62–72. https://doi.org/https://doi.org/10.1016/j.envsci.2016.08.002Vahdani, B., Veysmoradi, D., Noori, F., & Mansour, F. (2018a). Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. International Journal of Disaster Risk Reduction, 27(October 2017), 290–306. https://doi.org/10.1016/j.ijdrr.2017.10.015Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi, S. M. (2018b). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications, 30(3), 1–41. https://doi.org/10.1007/s00521-016-2696-7Vahdani, B., Veysmoradi, D., Shekari, N., & Amiri, M. (2022). Planning for relief distribution, victim evacuation, redistricting and service sharing under uncertainty. Socio-Economic Planning Sciences, 80, 835–854. https://doi.org/10.1016/j.seps.2021.101158Vahdat, K., Smith, N. J., & Amiri, G. G. (2014). Fuzzy multicriteria for developing a risk management system in seismically prone areas. Socio-Economic Planning Sciences, 48(4), 235–248. https://doi.org/10.1016/j.seps.2014.05.002Vega, D., & Roussat, C. (2015). Humanitarian logistics: The role of logistics service providers. International Journal of Physical Distribution and Logistics Management, 45(4), 352–375. https://doi.org/10.1108/IJPDLM-12-2014-0309Velasquez, J. D., Yoon, S. W., & Nof, S. Y. (2010). Computer-based collaborative training for transportation security and emergency response. Computers in Industry, 61(4), 380–389. https://doi.org/10.1016/j.compind.2009.12.007Venkatesh, V. G., Zhang, A., Deakins, E., Luthra, S., & Mangla, S. (2019a). A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains. Annals of Operations Research, 283(1–2), 1517–1550. https://doi.org/10.1007/s10479-018-2981-1Veysmoradi, D., Vahdani, B., Farhadi Sartangi, M., & Mousavi, S. M. (2018). Multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty. Scientia Iranica, 25(6), 3635-3653.Victoria, J. F., Afsar, H. M., & Prins, C. (2016). Column Generation based heuristic for the Vehicle Routing Problem with Time-Dependent Demand. IFAC-PapersOnLine, 49(12), 526–531. https://doi.org/10.1016/j.ifacol.2016.07.684Vodák, R., Bíl, M., & Křivánková, Z. (2018). A modified ant colony optimization algorithm to increase the speed of the road network recovery process after disasters. International Journal of Disaster Risk Reduction, 31(April), 1092–1106. https://doi.org/10.1016/j.ijdrr.2018.04.004Wang, J. W., Wang, H. F., Zhou, Y. M., Wang, Y., & Zhang, W. J. (2019). On an integrated approach to resilient transportation systems in emergency situations. Natural Computing, 18(4), 815–823. https://doi.org/10.1007/s11047-016-9605-yWang, Y. N., Liang, Y. Z., & Sun, H. (2020). A Regret Theory-Based Decision-Making Method for Urban Rail Transit in Emergency Response of Rainstorm Disaster. Journal of Advanced Transportation, 2020. https://doi.org/10.1155/2020/3235429Wijnmalen, D. J. D. (2007). Analysis of benefits, opportunities, costs, and risks (BOCR) with the AHP-ANP: A critical validation. Mathematical and Computer Modelling, 46(7–8), 892–905. https://doi.org/10.1016/j.mcm.2007.03.020Wohlgemuth, S., Oloruntoba, R., & Clausen, U. (2012). Dynamic vehicle routing with anticipation in disaster relief. Socio-Economic Planning Sciences, 46(4), 261–271. https://doi.org/10.1016/j.seps.2012.06.001Wu, J., & Wang, P. (2020). Post-disruption performance recovery to enhance resilience of interconnected network systems. Sustainable and Resilient Infrastructure.Wu, P., Cheng, J., & Feng, C. (2019). Resource-Constrained Emergency Scheduling for Forest Fires with Priority Areas: An Efficient Integer-Programming Approach. IEEJ Transactions on Electrical and Electronic Engineering, 14(2), 261–270. https://doi.org/10.1002/tee.22805Yan, J., Hu, B., Xie, K., Tai, H. M., & Li, W. (2020). Post-disaster power system restoration planning considering sequence dependent repairing period. International Journal of Electrical Power and Energy Systems, 117(August 2019). https://doi.org/10.1016/j.ijepes.2019.105612Yan, S., Chu, J. C., & Shih, Y.-L. (2014). Optimal scheduling for highway emergency repairs under large-scale supply-demand perturbations. IEEE Transactions on Intelligent Transportation Systems.Ye, Y., Liu, N., Hu, G. P., & Zhan, S. L. (2016). Scheduling algorithm based on follow-up sharing character for post-event response resource distribution in large-scal disasters. Journal of Systems Science and Systems Engineering, 25(1), 77–101. https://doi.org/10.1007/s11518-015-5284-yYe, Y., Liu, N., & Zhan, S.-L. (2013). Information update based sequential approach for emergency resources allocation planning. Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science).Yilmaz, H., & Kabak, Ö. (2020). Prioritizing distribution centers in humanitarian logistics using type-2 fuzzy MCDM approach. Journal of Enterprise Information Management, 33(5), 1199–1232. https://doi.org/10.1108/JEIM-09-2019-0310Yoon, K. P., & Kim, W. K. (2017a). The behavioral TOPSIS. Expert Systems with Applications, 89(December), 266–272. https://doi.org/10.1016/j.eswa.2017.07.045YV, Y. H., & DA, D. W. (1971). On a bicriterion formation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man and Cybernetics, 296–297.Zafri, N. M., Sameen, I., Jahangir, A., Tabassum, N., & Hasan, M. M. U. (2020). A multi-criteria decision-making approach for quantification of accessibility to market facilities in rural areas: an application in Bangladesh. GeoJournal, 0123456789. https://doi.org/10.1007/s10708-020-10161-zZagorecki, A., Ko, K., & Comfort, L. K. (2010). Interorganizational Information Exchange and Efficiency: Organizational Performance in Emergency Environments. Jasss-the Journal of Artificial Societies and Social Simulation, 13(3). https://doi.org/10.18564/jasss.1589Zhan, S. L., Liu, N., & Ye, Y. (2014). Coordinating efficiency and equity in disaster relief logistics via information updates. International Journal of Systems Science, 45(8), 1607–1621. https://doi.org/10.1080/00207721.2013.777490Zhang, C., Liu, X., Jiang, Y., Fan, B., & Song, X. (2016). A two-stage resource allocation model for lifeline systems quick response with vulnerability analysis. European Journal of Operational Research, 250(3), 855–864. https://doi.org/10.1016/j.ejor.2015.10.022Zhang, M.-X., Zhang, B., & Zheng, Y.-J. (2014). Bio-inspired meta-heuristics for emergency transportation problems. Algorithms, 7(1), 15–31. https://doi.org/10.3390/a7010015Zhang, P. Y., Liu, Y. K., Yang, G. Q., & Zhang, G. Q. (2020). A distributionally robust optimization model for designing humanitarian relief network with resource reallocation. Soft Computing, 24(4), 2749–2767. https://doi.org/10.1007/s00500-019-04362-zZhang, Z. K., Zhu, C. F., Wang, Q. R., & Yuan, J. S. (2019). Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory. Journal of Advanced Transportation, 2, 1-18. https://doi.org/10.1155/2019/2728073Zhou, Q., Huang, W. L., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science, 49(2), 243–252. https://doi.org/10.1016/j.ssci.2010.08.005Zhou, Y., Liu, J., Zhang, Y., & Gan, X. (2017). A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review, 99, 77–95. https://doi.org/https://doi.org/10.1016/j.tre.2016.12.011Zitzler, E., & Thiele, L. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. In IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (Vol. 3, Issue 4).Zou, Q., & Chen, S. (2020). Resilience Modeling of Interdependent Traffic-Electric Power System Subject to Hurricanes. Journal of Infrastructure Systems, 26(1). https://doi.org/10.1061/(ASCE)IS.1943-555X.0000524Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. 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