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Pre-positioning of relief items under road/facility vulnerability with concurrent restoration and relief transportation

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  • Ece Aslan
  • Melih Çelik

Abstract

Planning for response to sudden-onset disasters such as earthquakes, hurricanes, or floods needs to take into account the inherent uncertainties regarding the disaster and its impacts on the affected people as well as the logistics network. This article focuses on the design of a multi-echelon humanitarian response network, where the pre-disaster decisions of warehouse location and item pre-positioning are subject to uncertainties in relief item demand and vulnerability of roads and facilities following the disaster. Once the disaster strikes, relief transportation is accompanied by simultaneous repair of blocked roads, which delays the transportation process, but gradually increases the connectivity of the network at the same time. A two-stage stochastic program is formulated to model this system and a Sample Average Approximation (SAA) scheme is proposed for its heuristic solution. To enhance the efficiency of the SAA algorithm, we introduce a number of valid inequalities and bounds on the objective value. Computational experiments on a potential earthquake scenario in Istanbul, Turkey show that the SAA scheme is able to provide an accurate approximation of the objective function in reasonable time, and can help drive policy-based implications that may be applicable in preparation for similar potential disasters.

Suggested Citation

  • Ece Aslan & Melih Çelik, 2019. "Pre-positioning of relief items under road/facility vulnerability with concurrent restoration and relief transportation," IISE Transactions, Taylor & Francis Journals, vol. 51(8), pages 847-868, August.
    • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:8:p:847-868
    DOI: 10.1080/24725854.2018.1540900
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    Cited by:

    1. Davood Shiri & Vahid Akbari & F. Sibel Salman, 2020. "Online routing and scheduling of search-and-rescue teams," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 755-784, September.
    2. Dönmez, Zehranaz & Kara, Bahar Y. & Karsu, Özlem & Saldanha-da-Gama, Francisco, 2021. "Humanitarian facility location under uncertainty: Critical review and future prospects," Omega, Elsevier, vol. 102(C).
    3. Wei, Yian & Cheng, Yao & Liao, Haitao, 2024. "Optimal resilience-based restoration of a system subject to recurrent dependent hazards," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    4. Julia Monzón & Federico Liberatore & Begoña Vitoriano, 2020. "A Mathematical Pre-Disaster Model with Uncertainty and Multiple Criteria for Facility Location and Network Fortification," Mathematics, MDPI, vol. 8(4), pages 1-17, April.
    5. Canbilen Sütiçen, Tuğçe & Batun, Sakine & Çelik, Melih, 2023. "Integrated reinforcement and repair of interdependent infrastructure networks under disaster-related uncertainties," European Journal of Operational Research, Elsevier, vol. 308(1), pages 369-384.
    6. Alem, Douglas & Caunhye, Aakil M. & Moreno, Alfredo, 2022. "Revisiting Gini for equitable humanitarian logistics," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    7. Souza Almeida, Luana & Goerlandt, Floris & Pelot, Ronald, 2022. "Trends and gaps in the literature of road network repair and restoration in the context of disaster response operations," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    8. Yash V. Marthak & Eduardo Pérez & Francis A. Méndez Mediavilla, 2021. "A stochastic programming model for tactical product prepositioning at domestic hunger relief organizations impacted by natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2263-2291, July.
    9. Sayarshad, Hamid R. & Du, Xinpi & Gao, H. Oliver, 2020. "Dynamic post-disaster debris clearance problem with re-positioning of clearance equipment items under partially observable information," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 352-372.
    10. Ghavamifar, Ali & Torabi, S. Ali & Moshtari, Mohammad, 2022. "A hybrid relief procurement contract for humanitarian logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    11. Wang, Weiqiao & Yang, Kai & Yang, Lixing & Gao, Ziyou, 2021. "Two-stage distributionally robust programming based on worst-case mean-CVaR criterion and application to disaster relief management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    12. Qingwen Li & Jiuhe Wang & Yinggang Wang & Jian Lv, 2022. "A Two-Stage Stochastic Programming Model for Emergency Supplies Pre-Position under the Background of Civil-Military Integration," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    13. Hu, Shaolong & Dong, Zhijie Sasha & Dai, Rui, 2024. "A machine learning based sample average approximation for supplier selection with option contract in humanitarian relief," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    14. Rezapour, Shabnam & Farahani, Reza Zanjirani & Morshedlou, Nazanin, 2021. "Impact of timing in post-warning prepositioning decisions on performance measures of disaster management: A real-life application," European Journal of Operational Research, Elsevier, vol. 293(1), pages 312-335.
    15. 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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