IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0189411.html
   My bibliography  Save this article

Optimized maritime emergency resource allocation under dynamic demand

Author

Listed:
  • Wenfen Zhang
  • Xinping Yan
  • Jiaqi Yang

Abstract

Emergency resource is important for people evacuation and property rescue when accident occurs. The relief efforts could be promoted by a reasonable emergency resource allocation schedule in advance. As the marine environment is complicated and changeful, the place, type, severity of maritime accident is uncertain and stochastic, bringing about dynamic demand of emergency resource. Considering dynamic demand, how to make a reasonable emergency resource allocation schedule is challenging. The key problem is to determine the optimal stock of emergency resource for supplier centers to improve relief efforts. This paper studies the dynamic demand, and which is defined as a set. Then a maritime emergency resource allocation model with uncertain data is presented. Afterwards, a robust approach is developed and used to make sure that the resource allocation schedule performs well with dynamic demand. Finally, a case study shows that the proposed methodology is feasible in maritime emergency resource allocation. The findings could help emergency manager to schedule the emergency resource allocation more flexibly in terms of dynamic demand.

Suggested Citation

  • Wenfen Zhang & Xinping Yan & Jiaqi Yang, 2017. "Optimized maritime emergency resource allocation under dynamic demand," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-23, December.
    • Handle: RePEc:plo:pone00:0189411
    DOI: 10.1371/journal.pone.0189411
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189411
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0189411&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0189411?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
    ---><---

    References listed on IDEAS

    as
    1. Aharon, Ben-Tal & Boaz, Golany & Shimrit, Shtern, 2009. "Robust multi-echelon multi-period inventory control," European Journal of Operational Research, Elsevier, vol. 199(3), pages 922-935, December.
    2. Ai, Yun-fei & Lu, Jing & Zhang, Li-li, 2015. "The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 170-188.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Yanyan Wang & Baiqing Sun, 2022. "Multiperiod optimal emergency material allocation considering road network damage and risk under uncertain conditions," Operational Research, Springer, vol. 22(3), pages 2173-2208, July.
    2. Zhang, Lingye & Lu, Jing & Yang, Zaili, 2021. "Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks," European Journal of Operational Research, Elsevier, vol. 293(2), pages 529-546.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    2. Sarhadi, Hassan & Naoum-Sawaya, Joe & Verma, Manish, 2020. "A robust optimization approach to locating and stockpiling marine oil-spill response facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    3. Bo Dai & Fenfen Li, 2021. "Joint Inventory Replenishment Planning of an E-Commerce Distribution System with Distribution Centers at Producers’ Locations," Logistics, MDPI, vol. 5(3), pages 1-14, July.
    4. Zhang, Wei & (Ato) Xu, Wangtu, 2017. "Simulation-based robust optimization for the schedule of single-direction bus transit route: The design of experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 203-230.
    5. Metzker Soares, Paula & Thevenin, Simon & Adulyasak, Yossiri & Dolgui, Alexandre, 2024. "Adaptive robust optimization for lot-sizing under yield uncertainty," European Journal of Operational Research, Elsevier, vol. 313(2), pages 513-526.
    6. Zhang, Lingye & Lu, Jing & Yang, Zaili, 2021. "Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks," European Journal of Operational Research, Elsevier, vol. 293(2), pages 529-546.
    7. Viktoryia Buhayenko & Dick den Hertog, 2017. "Adjustable Robust Optimisation approach to optimise discounts for multi-period supply chain coordination under demand uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6801-6823, November.
    8. Guerrero, W.J. & Yeung, T.G. & Guéret, C., 2013. "Joint-optimization of inventory policies on a multi-product multi-echelon pharmaceutical system with batching and ordering constraints," European Journal of Operational Research, Elsevier, vol. 231(1), pages 98-108.
    9. Oğuz Solyalı & Jean-François Cordeau & Gilbert Laporte, 2016. "The Impact of Modeling on Robust Inventory Management Under Demand Uncertainty," Management Science, INFORMS, vol. 62(4), pages 1188-1201, April.
    10. 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.
    11. Shin, Youngchul & Lee, Sangyoon & Moon, Ilkyeong, 2021. "Robust multiperiod inventory model with a new type of buy one get one promotion: “My Own Refrigerator”," Omega, Elsevier, vol. 99(C).
    12. Moshe Sniedovich, 2010. "A bird's view of info-gap decision theory," Journal of Risk Finance, Emerald Group Publishing, vol. 11(3), pages 268-283, May.
    13. Almaraj, Ismail I. & Trafalis, Theodore B., 2019. "An integrated multi-echelon robust closed- loop supply chain under imperfect quality production," International Journal of Production Economics, Elsevier, vol. 218(C), pages 212-227.
    14. Mumtaz Karatas & Nasuh Razi & Murat M. Gunal, 2017. "An ILP and simulation model to optimize search and rescue helicopter operations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1335-1351, November.
    15. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    16. Gülpınar, Nalan & Pachamanova, Dessislava & Çanakoğlu, Ethem, 2013. "Robust strategies for facility location under uncertainty," European Journal of Operational Research, Elsevier, vol. 225(1), pages 21-35.
    17. Xin, Linwei & Goldberg, David A., 2021. "Time (in)consistency of multistage distributionally robust inventory models with moment constraints," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1127-1141.
    18. Liu, Jia & Bai, Jinyu & Wu, Desheng, 2021. "Medical supplies scheduling in major public health emergencies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    19. Farough Motamed Nasab & Zukui Li, 2023. "Multistage Adaptive Robust Binary Optimization: Uncertainty Set Lifting versus Partitioning through Breakpoints Optimization," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    20. Oğuz Solyalı & Jean-François Cordeau & Gilbert Laporte, 2012. "Robust Inventory Routing Under Demand Uncertainty," Transportation Science, INFORMS, vol. 46(3), pages 327-340, August.

    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:plo:pone00:0189411. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.