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A multi-stage stochastic programming model for relief distribution considering the state of road network

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  • Hu, Shaolong
  • Han, Chuanfeng
  • Dong, Zhijie Sasha
  • Meng, Lingpeng

Abstract

As an important aspect in disaster operations management, relief distribution has been challenged by lots of factors, such as unpredictable occurrence time, intensity and location of secondary disasters (e.g. aftershocks and landslides, which usually occur after an earthquake), and availability of vehicles. A multi-stage stochastic programming model is developed for disaster relief distribution with consideration of multiple types of vehicles, fluctuation of rental, and the state of road network. The state of road network is characterized using uncertain and dynamic road capacity. The scenario tree is employed to represent the uncertain and dynamic road capacity, and demonstrate the decision process of relief distribution. A progressive hedging algorithm (PHA) is proposed for solving the proposed model in large-scale size. Based on a real-world case of Yaan earthquake in China, numerical experiments are presented to study the applicability of the proposed model and demonstrate the effectiveness of the proposed PHA. Useful managerial insights are provided by conducting numerical analysis.

Suggested Citation

  • Hu, Shaolong & Han, Chuanfeng & Dong, Zhijie Sasha & Meng, Lingpeng, 2019. "A multi-stage stochastic programming model for relief distribution considering the state of road network," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 64-87.
    • Handle: RePEc:eee:transb:v:123:y:2019:i:c:p:64-87
    DOI: 10.1016/j.trb.2019.03.014
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    10. Hu, Shaolong & Dong, Zhijie Sasha & Lev, Benjamin, 2022. "Supplier selection in disaster operations management: Review and research gap identification," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
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    13. Zhang, Guowei & Zhu, Ning & Ma, Shoufeng & Xia, Jun, 2021. "Humanitarian relief network assessment using collaborative truck-and-drone system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    14. Zhongxiu Peng & Cong Wang & Wenqing Xu & Jinsong Zhang, 2022. "Research on Location-Routing Problem of Maritime Emergency Materials Distribution Based on Bi-Level Programming," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    15. Zohaib Hassan & Irtiza Ali Shah & Ahsan Sarwar Rana, 2022. "Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 103-121, June.
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    18. Zhang, Guowei & Jia, Ning & Zhu, Ning & Adulyasak, Yossiri & Ma, Shoufeng, 2023. "Robust drone selective routing in humanitarian transportation network assessment," European Journal of Operational Research, Elsevier, vol. 305(1), pages 400-428.
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    21. Peiyu Zhang & Yankui Liu & Guoqing Yang & Guoqing Zhang, 2022. "A multi-objective distributionally robust model for sustainable last mile relief network design problem," Annals of Operations Research, Springer, vol. 309(2), pages 689-730, February.

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