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A stochastic programming model for emergency supply planning considering traffic congestion

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  • Qingyi Wang
  • Xiaofeng Nie

Abstract

Traffic congestion is one key factor that delays emergency supply logistics after disasters, but it is seldom explicitly considered in previous emergency supply planning models. To fill the gap, we incorporate traffic congestion effects and propose a two-stage location-allocation model that facilitates the planning of emergency supplies pre-positioning and post-disaster transportation. The formulated mixed-integer nonlinear programming model is solved by applying the generalized Benders decomposition algorithm, and the suggested approach outperforms the direct solving strategy. With a case study on a hurricane threat in the southeastern USA, we illustrate that our traffic congestion incorporated model is a meaningful generalization of a previous emergency supply planning model in the literature. Finally, managerial insights about the supplies pre-positioning plan and traffic control policy are discussed.

Suggested Citation

  • Qingyi Wang & Xiaofeng Nie, 2019. "A stochastic programming model for emergency supply planning considering traffic congestion," IISE Transactions, Taylor & Francis Journals, vol. 51(8), pages 910-920, August.
    • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:8:p:910-920
    DOI: 10.1080/24725854.2019.1589657
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    Cited by:

    1. Wang, Weiqiao & Yang, Kai & Yang, Lixing & Gao, Ziyou, 2023. "Distributionally robust chance-constrained programming for multi-period emergency resource allocation and vehicle routing in disaster response operations," Omega, Elsevier, vol. 120(C).
    2. 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.
    3. 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).
    4. Liu, Kanglin & Yang, Liu & Zhao, Yejia & Zhang, Zhi-Hai, 2023. "Multi-period stochastic programming for relief delivery considering evolving transportation network and temporary facility relocation/closure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    5. Yu, Wuyang, 2023. "A robust model for emergency supplies prepositioning and transportation considering road disruptions," Operations Research Perspectives, Elsevier, vol. 10(C).
    6. Fu, Yaping & Wu, Di & Wang, Yan & Wang, Hongfeng, 2020. "Facility location and capacity planning considering policy preference and uncertain demand under the One Belt One Road initiative," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 172-186.
    7. 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).
    8. Yin, Yunqiang & Yang, Yongjian & Yu, Yugang & Wang, Dujuan & Cheng, T.C.E., 2023. "Robust vehicle routing with drones under uncertain demands and truck travel times in humanitarian logistics," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    9. Wang, Qingyi & Nie, Xiaofeng, 2022. "A stochastic programming model for emergency supply planning considering transportation network mitigation and traffic congestion," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    10. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).

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