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Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations

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Abstract

In this study, we propose a mathematical model and heuristics for solving a multi-period location-allocation problem in post-disaster operations, which takes into account the impact of distribution over the population. Logistics restrictions such as human and financial resources are considered. In addition, a brief review on resilience system models is provided, as well as their connection with quantitative models for post-disaster relief operations. In particular, we highlight how one can improve resilience by means of OR/MS strategies. Then, a simpler resilience schema is proposed, which better reflects an active system for providing humanitarian aid in post-disaster operations, similar to the model focused in this work. The proposed model is non-linear and solved by a decomposition approach: the master level problem is addressed by a non-linear solver, while the slave subproblem is treated as a black-box coupling heuristics and a Variable Neighborhood Descent local search. Computational experiments have been done using several scenarios, and real data from Belo Horizonte city in Brazil.

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Notes

  1. https://www.disasterscharter.org.

References

  • Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location-transportation problem for disaster response. Computers and Operations Research, 41, 83–93.

    Article  Google Scholar 

  • Altay, N., & Green, W. G, I. I. I. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475–493.

    Article  Google Scholar 

  • Audet, C., & Dennis, J. E, Jr. (2006). Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization, 17(1), 188–217.

    Article  Google Scholar 

  • Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 12(2), 51–63.

    Article  Google Scholar 

  • Berkoune, D., Renaud, J., Rekik, M., & Ruiz, A. (2012). Transportation in disaster response operations. Socio-Economic Planning Sciences, 46, 23–32.

    Article  Google Scholar 

  • Boin, A., & McConnell, A. (2007). Preparing for critical infrastructure breakdowns: The limits of crisis management and the need for resilience. Journal of Contingencies and Crisis Management, 15(1), 50–59.

    Article  Google Scholar 

  • Bruneau, M., Eeri, A., Chang, S., Eeri, B., Ronald, T., Eeri, C., et al. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19, 733–752.

    Article  Google Scholar 

  • Bruneau, M., & Reinhorn, A. (2007). Exploring the concept of seismic resilience for acute care facilities. Earthquake Spectra, 23(1), 41–62.

    Article  Google Scholar 

  • Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4–13. (Special Issue: Disaster Planning and Logistics: Part 1).

    Article  Google Scholar 

  • Diaz, R., Behr, J., Toba, A. L., Giles, B., Ng, M., Longo, F., et al. (2013). Humanitarian/emergency logistics models: A state of the art overview. In Proceedings of the 2013 Summer Computer Simulation Conference, SCSC ’13 (pp. 1–8). Society for Modeling & Simulation International, Vista, CA.

  • Francis, R., & Bekera, B. (2014). Resilience analysis for engineered and infrastructure systems under deep uncertainty or emergent conditions. Reliability Engineering and System Safety, 121, 90–103.

    Article  Google Scholar 

  • Haimes, Y. Y. (2009). On the definition of resilience in systems. Risk Analysis, 29(4), 498–501.

    Article  Google Scholar 

  • Hanson, B., & Roberts, L. (2005). Resiliency in the face of disaster. Science, 309(5737), 1029.

    Article  Google Scholar 

  • Holling, C. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4(1), 1–23.

    Article  Google Scholar 

  • Horner, M. W., & Downs, J. A. (2010). Optimizing hurricane disaster relief goods distribution: Model development and application with respect to planning strategies. Disasters, 34(3), 821–844.

    Article  Google Scholar 

  • Huachi, P., Santos, A. C., & Prins, C. (2015). Solving multiple depot heterogeneous fleet vehicle routing problems for post disaster relief. In Proceedings of the 6th international triennial workshop on Freight Transportation and Logistics, Odysseus 2015 (p. 4p). Ajaccio, France.

  • 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.

    Article  Google Scholar 

  • Huang, R., Kim, S., & Menezes, M. (2010). Facility location for large-scale emergencies. Annals of Operations Research, 181(1), 271–286.

    Article  Google Scholar 

  • Jia, H., Ordóñez, F., & Dessouky, M. (2007). A modeling framework for facility location of medical services for large-scale emergencies. IIE Transactions on Homeland Security, 39(1), 41–55.

    Google Scholar 

  • Jia, H., Ordóñez, F., & Dessouky, M. (2007). Solution approaches for facility location of medical supplies for large-scale emergencies. Computers and Industrial Engineering, 52(2), 257–276.

    Article  Google Scholar 

  • Le Digabel, S. (2011). Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 37(4), 1–15.

    Article  Google Scholar 

  • Liberatore, F., Ortuño, M., Tirado, G., Vitoriano, B., & Scaparra, M. (2014). A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in humanitarian logistics. Computers and Operations Research, 42, 3–13.

    Article  Google Scholar 

  • Manyena, S. (2006). The concept of resilience revisited. Disasters, 30(4), 434–450.

    Article  Google Scholar 

  • Mezzou, O., Birregah, B., & Châtelet, E. (2011). A theoretical study of the interactions between the components of resilience in critical urban infrastructures. In IET international conference on smart and sustainable city (ICSSC 2011) (pp. 1–6).

  • Mladenovic, N., & Hansen, P. (1997). Variable neighborhood search. Computers and Operations Research, 24(11), 1097–1100.

    Article  Google Scholar 

  • Murat, H., Prins, C., & Santos, A. (2014). Exact and heuristic algorithms for solving the generalized vehicle routing problem with flexible fleet size. International Transactions in Operational Research, 21(1), 153–175.

    Article  Google Scholar 

  • Nolz, P., Doerner, K., Gutjahr, W., & Hartl, R. (2010). A bi-objective metaheuristic for disaster relief operation planning. In C. C. Coello, C. Dhaenens, & L. Jourdan (Eds.), Advances in multi-objective nature inspired computing. Studies in computational intelligence (Vol. 272, pp. 167–187). Berlin: Springer.

    Google Scholar 

  • Ortuño, M. T., Cristóbal, P., Ferrer, J. M., Martín-Campo, F. J., Muñoz, S., Tirado, G., et al. (2013). Decision aid models and systems for humanitarian logistics. A survey. In B. Vitoriano, J. Montero, & D. Ruan (Eds.), Decision aid models for disaster management and emergencies. Atlantis computational intelligence systems (Vol. 7, pp. 17–44). amsterdam: Atlantis Press.

    Chapter  Google Scholar 

  • Prins, C., Santos, A., & Afsar, H. (2012). Splitting procedure and a relaxed iterated local search for the generalized vehicle routing problem. In Procedings of the Latino-Iberoamericano de Investigación Operativa, Simpósio Brasileiro de Pesquisa Operacional, CLAIO-SBPO. Rio de Janeiro, Brazil.

  • Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location-routing problem in disaster relief. Computers and Operations Research, 42, 25–39.

    Article  Google Scholar 

  • Rekik, M., Ruiz, A., Renaud, J., Berkoune, D., & Paquet, S. (2013). A decision support system for humanitarian network design and distribution operations. In V. Zeimpekis, S. Ichoua, & I. Minis (Eds.), Humanitarian and relief logistics. Operations research/computer science interfaces series (Vol. 54, pp. 1–20). New York: Springer.

    Google Scholar 

  • Rottkemper, B., Fischer, K., & Blecken, A. (2012). A transshipment model for distribution and inventory relocation under uncertainty in humanitarian operations. Socio-Economic Planning Sciences, 46, 98–109.

    Article  Google Scholar 

  • Sheu, J. B. (2010). Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 46(1), 1–17.

    Article  Google Scholar 

  • Tzeng, G. H., Cheng, H. J., & Huang, T. D. (2007). Multi-objective optimal planning for designing relief delivery systems. Transportation Research Part E: Logistics and Transportation Review, 43(6), 673–686.

    Article  Google Scholar 

  • Vitoriano, B., Ortuño, M. T., Tirado, G., & Montero, J. (2011). A multi-criteria optimization model for humanitarian aid distribution. Journal of Global Optimization, 51(2), 189–208.

    Article  Google Scholar 

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Acknowledgments

This work was supported by grants from the CSFRS (Conseil Supérieur de la Formation et de la Recherche Stratégiques, France) and from the PST Résilience et Gestion de Crise at the UTT, France.

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Correspondence to Andréa Cynthia Santos.

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Duhamel, C., Santos, A.C., Brasil, D. et al. Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations. Ann Oper Res 247, 693–713 (2016). https://doi.org/10.1007/s10479-015-2104-1

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