IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v131y2025ics0305048324001956.html
   My bibliography  Save this article

Pre-occurrence location-allocation-configuration of maritime emergency resources considering shipborne unmanned aerial vehicle (UAV)

Author

Listed:
  • Hu, Yuzhen
  • Wang, Min
  • Guo, Xinghai
  • Lukinykh, Valery F.

Abstract

Demand for maritime transportation has constantly increased throughout recent years due to its high capacity, extensive coverage, high safety, and so on. However, the growth has also contributed to a rise in maritime accidents, highlighting the need for developing maritime emergency management strategies. In maritime emergency management, resource location-allocation-configuration is the most important and inseparable preparedness action before disasters, directly deciding the quality of emergency rescue operations. Traditional ship-only rescue is frequently both cost-intensive and time-consuming, seriously affecting rescue performance. This study introduces an innovative shipborne UAV operation system to address these limitations. Firstly, a multi-objective mixed-integer nonlinear programming model is established to minimize total rescue time and cost by considering the uncertain maritime environment, continuous docking placements of ships, UAV flight distance, rescue station capacity, etc. Secondly, to efficiently obtain an execution plan, a two-stage algorithm is proposed to facilitate our mission optimization model. Large-scale simulated instances and a real case study demonstrate that (i) the proposed algorithm can solve the model efficiently. (ii) the application of shipborne UAV operation system in maritime emergencies shows a significant improvement in time and cost compared to ship-only system. (iii) it is necessary to consider the uncertain maritime environment when solving emergency management problems. Moreover, extensive parameter sensitivity analysis provides managerial insights into the impact of various factors on the optimization outcomes. We also explore deeper insights that may benefit maritime administration's decision support.

Suggested Citation

  • Hu, Yuzhen & Wang, Min & Guo, Xinghai & Lukinykh, Valery F., 2025. "Pre-occurrence location-allocation-configuration of maritime emergency resources considering shipborne unmanned aerial vehicle (UAV)," Omega, Elsevier, vol. 131(C).
    • Handle: RePEc:eee:jomega:v:131:y:2025:i:c:s0305048324001956
    DOI: 10.1016/j.omega.2024.103231
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048324001956
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2024.103231?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.

    References listed on IDEAS

    as
    1. Duma, Davide & Aringhieri, Roberto, 2023. "Real-time resource allocation in the emergency department: A case study," Omega, Elsevier, vol. 117(C).
    2. 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).
    3. Shi, Yong & Yang, Junhao & Han, Qian & Song, Hao & Guo, Haixiang, 2024. "Optimal decision-making of post-disaster emergency material scheduling based on helicopter–truck–drone collaboration," Omega, Elsevier, vol. 127(C).
    4. 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.
    5. L N Van Wassenhove, 2006. "Humanitarian aid logistics: supply chain management in high gear," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 475-489, May.
    6. 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.
    7. Razi, Nasuh & Karatas, Mumtaz, 2016. "A multi-objective model for locating search and rescue boats," European Journal of Operational Research, Elsevier, vol. 254(1), pages 279-293.
    8. Stienen, V.F. & Wagenaar, J.C. & den Hertog, D. & Fleuren, H.A., 2021. "Optimal depot locations for humanitarian logistics service providers using robust optimization," Omega, Elsevier, vol. 104(C).
    9. Wagenaar, J.C. & Fragkos, I. & Faro, W.L.C., 2023. "Transportation asset acquisition under a newsvendor model with cutting-stock restrictions: Approximation and decomposition algorithms," Other publications TiSEM 97eddbd0-6e34-489c-b27d-9, Tilburg University, School of Economics and Management.
    10. Nabila Azi & Michel Gendreau & Jean-Yves Potvin, 2012. "A dynamic vehicle routing problem with multiple delivery routes," Annals of Operations Research, Springer, vol. 199(1), pages 103-112, October.
    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. Baode Li & Jing Lu & Han Lu & Jing Li, 2023. "Predicting maritime accident consequence scenarios for emergency response decisions using optimization-based decision tree approach," Maritime Policy & Management, Taylor & Francis Journals, vol. 50(1), pages 19-41, January.
    13. Verma, Manish & Gendreau, Michel & Laporte, Gilbert, 2013. "Optimal location and capability of oil-spill response facilities for the south coast of Newfoundland," Omega, Elsevier, vol. 41(5), pages 856-867.
    14. Dell’Amico, Mauro & Montemanni, Roberto & Novellani, Stefano, 2021. "Algorithms based on branch and bound for the flying sidekick traveling salesman problem," Omega, Elsevier, vol. 104(C).
    15. Sakineh Lakzaei & Donya Rahmani & Babak Mohamadpour Tosarkani & Sepideh Nasiri, 2023. "Integrated optimal scheduling and routing of repair crew and relief vehicles after disaster: a novel hybrid solution approach," Annals of Operations Research, Springer, vol. 328(2), pages 1495-1522, September.
    16. Zhu, Jianxin & Zhang, Weidan & Yu, Lean & Guo, Xinghai, 2024. "A novel multi-attention reinforcement learning for the scheduling of unmanned shipment vessels (USV) in automated container terminals," Omega, Elsevier, vol. 129(C).
    17. Özdamar, Linet & Demir, Onur, 2012. "A hierarchical clustering and routing procedure for large scale disaster relief logistics planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 591-602.
    18. Kovalyov, Mikhail Y. & Pesch, Erwin, 2014. "A game mechanism for single machine sequencing with zero risk," Omega, Elsevier, vol. 44(C), pages 104-110.
    19. Benjamin Lev & Howard J. Weiss, 1990. "Inventory Models with Cost Changes," Operations Research, INFORMS, vol. 38(1), pages 53-63, February.
    20. Sezgi Tekil-Ergün & Erwin Pesch & Katarzyna Anna Kuzmicz, 2022. "Solving a hybrid mixed fleet heterogeneous dial-a-ride problem in delay-sensitive container transportation," International Journal of Production Research, Taylor & Francis Journals, vol. 60(1), pages 297-323, January.
    21. Duma, Davide & Aringhieri, Roberto, 2019. "The management of non-elective patients: shared vs. dedicated policies," Omega, Elsevier, vol. 83(C), pages 199-212.
    22. Stefan Poikonen & Bruce Golden & Edward A. Wasil, 2019. "A Branch-and-Bound Approach to the Traveling Salesman Problem with a Drone," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 335-346, April.
    23. J.H. Ruan & X.P. Wang & F.T.S. Chan & Y. Shi, 2016. "Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4368-4386, July.
    24. Mbiadou Saleu, Raïssa G. & Deroussi, Laurent & Feillet, Dominique & Grangeon, Nathalie & Quilliot, Alain, 2022. "The parallel drone scheduling problem with multiple drones and vehicles," European Journal of Operational Research, Elsevier, vol. 300(2), pages 571-589.
    25. Grot, Matthias, 2024. "Decision support framework for tactical emergency medical service location planning," Omega, Elsevier, vol. 125(C).
    26. Niels Agatz & Paul Bouman & Marie Schmidt, 2018. "Optimization Approaches for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 52(4), pages 965-981, August.
    27. Stefan Poikonen & Bruce Golden, 2020. "The Mothership and Drone Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 249-262, April.
    28. Wang, Kai & Pesch, Erwin & Kress, Dominik & Fridman, Ilia & Boysen, Nils, 2022. "The Piggyback Transportation Problem: Transporting drones launched from a flying warehouse," European Journal of Operational Research, Elsevier, vol. 296(2), pages 504-519.
    29. Xia, Jun & Wang, Kai & Wang, Shuaian, 2019. "Drone scheduling to monitor vessels in emission control areas," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 174-196.
    30. Dezhi Zhang & Yarui Zhang & Shuanglin Li & Shuangyan Li & Wanru Chen, 2024. "Bi-objective robust optimisation on relief collaborative distribution considering secondary disasters," International Journal of Production Research, Taylor & Francis Journals, vol. 62(7), pages 2435-2454, April.
    Full references (including those not matched with items on IDEAS)

    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. Shi, Yong & Yang, Junhao & Han, Qian & Song, Hao & Guo, Haixiang, 2024. "Optimal decision-making of post-disaster emergency material scheduling based on helicopter–truck–drone collaboration," Omega, Elsevier, vol. 127(C).
    2. Madani, Batool & Ndiaye, Malick & Salhi, Said, 2024. "Hybrid truck-drone delivery system with multi-visits and multi-launch and retrieval locations: Mathematical model and adaptive variable neighborhood search with neighborhood categorization," European Journal of Operational Research, Elsevier, vol. 316(1), pages 100-125.
    3. Jiang, Jie & Dai, Ying & Yang, Fei & Ma, Zujun, 2024. "A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services," European Journal of Operational Research, Elsevier, vol. 312(1), pages 125-137.
    4. Zhu, Waiming & Hu, Xiaoxuan & Pei, Jun & Pardalos, Panos M., 2024. "Minimizing the total travel distance for the locker-based drone delivery: A branch-and-cut-based method," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
    5. Ren, Xuan & Froger, Aurélien & Jabali, Ola & Liang, Gongqian, 2024. "A competitive heuristic algorithm for vehicle routing problems with drones," European Journal of Operational Research, Elsevier, vol. 318(2), pages 469-485.
    6. Yu, Shaohua & Puchinger, Jakob & Sun, Shudong, 2024. "Electric van-based robot deliveries with en-route charging," European Journal of Operational Research, Elsevier, vol. 317(3), pages 806-826.
    7. Zhao, Lei & Bi, Xinhua & Li, Gendao & Dong, Zhaohui & Xiao, Ni & Zhao, Anni, 2022. "Robust traveling salesman problem with multiple drones: Parcel delivery under uncertain navigation environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    8. Tiniç, Gizem Ozbaygin & Karasan, Oya E. & Kara, Bahar Y. & Campbell, James F. & Ozel, Aysu, 2023. "Exact solution approaches for the minimum total cost traveling salesman problem with multiple drones," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 81-123.
    9. Amine Masmoudi, M. & Mancini, Simona & Baldacci, Roberto & Kuo, Yong-Hong, 2022. "Vehicle routing problems with drones equipped with multi-package payload compartments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    10. Khalili-Fard, Alireza & Hashemi, Mojgan & Bakhshi, Alireza & Yazdani, Maziar & Jolai, Fariborz & Aghsami, Amir, 2024. "Integrated relief pre-positioning and procurement planning considering non-governmental organizations support and perishable relief items in a humanitarian supply chain network," Omega, Elsevier, vol. 127(C).
    11. 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).
    12. Güneş Erdoğan & E. Alper Y?ld?r?m, 2021. "Exact and Heuristic Algorithms for the Carrier–Vehicle Traveling Salesman Problem," Transportation Science, INFORMS, vol. 55(1), pages 101-121, 1-2.
    13. Nguyen, Minh Anh & Dang, Giang Thi-Huong & Hà, Minh Hoàng & Pham, Minh-Trien, 2022. "The min-cost parallel drone scheduling vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 299(3), pages 910-930.
    14. Zhang, Juan & Campbell, James F. & Sweeney, Donald C., 2024. "A continuous approximation approach to integrated truck and drone delivery systems," Omega, Elsevier, vol. 126(C).
    15. Yu, Shaohua & Puchinger, Jakob & Sun, Shudong, 2022. "Van-based robot hybrid pickup and delivery routing problem," European Journal of Operational Research, Elsevier, vol. 298(3), pages 894-914.
    16. Michael Dienstknecht & Nils Boysen & Dirk Briskorn, 2022. "The traveling salesman problem with drone resupply," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1045-1086, December.
    17. Archetti, Claudia & Peirano, Lorenzo & Speranza, M. Grazia, 2022. "Optimization in multimodal freight transportation problems: A Survey," European Journal of Operational Research, Elsevier, vol. 299(1), pages 1-20.
    18. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    19. Cai, Lei & Li, Jiliu & Wang, Kai & Luo, Zhixing & Qin, Hu, 2025. "Optimal allocation and route design for station-based drone inspection of large-scale facilities," Omega, Elsevier, vol. 130(C).
    20. Liu, Zeyu & Li, Xueping & Khojandi, Anahita, 2022. "The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

    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:eee:jomega:v:131:y:2025:i:c:s0305048324001956. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.