IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v51y2019i8p910-920.html
   My bibliography  Save this article

A stochastic programming model for emergency supply planning considering traffic congestion

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24725854.2019.1589657
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24725854.2019.1589657?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    4. Yu, Wuyang, 2023. "A robust model for emergency supplies prepositioning and transportation considering road disruptions," Operations Research Perspectives, Elsevier, vol. 10(C).
    5. 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.
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:uiiexx:v:51:y:2019:i:8:p:910-920. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.