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A dynamic model for disaster response considering prioritized demand points

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  • Rivera-Royero, Daniel
  • Galindo, Gina
  • Yie-Pinedo, Ruben

Abstract

This paper addresses the problem of distributing relief supplies after the occurrence of a disaster. We develop a dynamic model to serve demand, while prioritizing the response, according to the level of urgency of demand points. Our model is thought to be applied during a planning horizon and it considers dynamic demand, capacity constraints and priorities. To evaluate the applicability of our model, we use a real case study of a flood occurred in Colombia. We also test the computational solvability of our model and we propose and test different solution methodologies for solving larger instances of our problem.

Suggested Citation

  • Rivera-Royero, Daniel & Galindo, Gina & Yie-Pinedo, Ruben, 2016. "A dynamic model for disaster response considering prioritized demand points," Socio-Economic Planning Sciences, Elsevier, vol. 55(C), pages 59-75.
    • Handle: RePEc:eee:soceps:v:55:y:2016:i:c:p:59-75
    DOI: 10.1016/j.seps.2016.07.001
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    7. Yanjin He & Hosang Jung, 2018. "A Voting TOPSIS Approach for Determining the Priorities of Areas Damaged in Disasters," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    8. Christoph Stallkamp & Florian Diehlmann & Markus Lüttenberg & Marcus Wiens & Rebekka Volk & Frank Schultmann, 2022. "On the combination of water emergency wells and mobile treatment systems: a case study of the city of Berlin," Annals of Operations Research, Springer, vol. 319(1), pages 259-290, December.
    9. Diehlmann, Florian & Hiemsch, Patrick S. & Wiens, Marcus & Lüttenberg, Markus & Schultmann, Frank, 2020. "A novel approach to include social costs in humanitarian objective functions," Working Paper Series in Production and Energy 52, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    10. Xuehong Gao, 2022. "A bi-level stochastic optimization model for multi-commodity rebalancing under uncertainty in disaster response," Annals of Operations Research, Springer, vol. 319(1), pages 115-148, December.
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    14. Rivera-Royero, Daniel & Galindo, Gina & Yie-Pinedo, Ruben, 2020. "Planning the delivery of relief supplies upon the occurrence of a natural disaster while considering the assembly process of the relief kits," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    15. Yanbin Chang & Yongjia Song & Burak Eksioglu, 2022. "A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty," Annals of Operations Research, Springer, vol. 319(1), pages 1231-1263, December.
    16. Rodríguez-Espíndola, Oscar & Ahmadi, Hossein & Gastélum-Chavira, Diego & Ahumada-Valenzuela, Omar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel, 2023. "Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    17. Cotes, Nathalie & Cantillo, Victor, 2019. "Including deprivation costs in facility location models for humanitarian relief logistics," Socio-Economic Planning Sciences, Elsevier, vol. 65(C), pages 89-100.
    18. Wang, Qingyi & Liu, Zhuomeng & Jiang, Peng & Luo, Li, 2022. "A stochastic programming model for emergency supplies pre-positioning, transshipment and procurement in a regional healthcare coalition," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    19. Suzuki, Yoshinori, 2020. "Impact of material convergence on last-mile distribution in humanitarian logistics," International Journal of Production Economics, Elsevier, vol. 223(C).
    20. Cantillo, Victor & Serrano, Iván & Macea, Luis F. & Holguín-Veras, José, 2018. "Discrete choice approach for assessing deprivation cost in humanitarian relief operations," Socio-Economic Planning Sciences, Elsevier, vol. 63(C), pages 33-46.
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    22. Acar, Müge & Kaya, Onur, 2023. "Dynamic inventory decisions for humanitarian aid materials considering budget limitations," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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