IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v197y2025ics1366554525001061.html
   My bibliography  Save this article

Cooperative game based heterogeneous tasks planning for UAV swarms in edge environments

Author

Listed:
  • Gao, Jiajing
  • Zhao, Jianyi
  • Zhao, Zheng
  • Cheng, Junkai
  • Zhen, Lu

Abstract

We investigate the cooperative planning of heterogeneous tasks for UAV swarms in edge environments based on cooperative game theory. To solve this problem, we propose a solution method based on column generation and iteration. A hybrid centralized and distributed solution framework is designed to enhance the efficiency of the overall task allocation system. At the early stage of disaster response, a centralized task allocation method is adopted, combined with a mixed integer planning model for task allocation, and a column generation-based approach is designed to solve the model. As the rescue work advances, a distributed task reallocation approach is adopted by considering the cooperation game, and the Shapley value is used to obtain the revenue allocation when the UAV swarms cooperate. The two phases collaborate with each other and continuously update the task allocation scheme through iterative solving. We design a solution method based on column generation and iteration to obtain task allocation scheme. We verify the advantages of our method in terms of computation time and solution quality through numerical experiments. We also validate the benefits of UAV swarm cooperation. We also examine the effect of variations in the scale and parameters of the instances on the total revenue.

Suggested Citation

  • Gao, Jiajing & Zhao, Jianyi & Zhao, Zheng & Cheng, Junkai & Zhen, Lu, 2025. "Cooperative game based heterogeneous tasks planning for UAV swarms in edge environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
  • Handle: RePEc:eee:transe:v:197:y:2025:i:c:s1366554525001061
    DOI: 10.1016/j.tre.2025.104065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554525001061
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tre.2025.104065?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. Marelot H. de Vos & Rolf N. van Lieshout & Twan Dollevoet, 2024. "Electric Vehicle Scheduling in Public Transit with Capacitated Charging Stations," Transportation Science, INFORMS, vol. 58(2), pages 279-294, March.
    2. Amirsahami, Amirali & Barzinpour, Farnaz & Pishvaee, Mir Saman, 2025. "A fuzzy programming model for decentralization and drone utilization in urban humanitarian relief chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(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, 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).
    5. Lu Zhen & Jiajing Gao & Zheyi Tan & Shuaian Wang & Roberto Baldacci, 2023. "Branch-price-and-cut for trucks and drones cooperative delivery," IISE Transactions, Taylor & Francis Journals, vol. 55(3), pages 271-287, March.
    6. Zheng, Hankun & Sun, Huijun & Kang, Liujiang & Dai, Peiling & Wu, Jianjun, 2023. "Multi-route coordination for bus systems in response to road disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    7. Gao, Jiajing & Zhen, Lu & Laporte, Gilbert & He, Xueting, 2023. "Scheduling trucks and drones for cooperative deliveries," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    8. Dukkanci, Okan & Koberstein, Achim & Kara, Bahar Y., 2023. "Drones for relief logistics under uncertainty after an earthquake," European Journal of Operational Research, Elsevier, vol. 310(1), pages 117-132.
    9. Jin, Zhongyi & Ng, Kam K.H. & Zhang, Chenliang & Liu, Wei & Zhang, Fangni & Xu, Gangyan, 2024. "A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    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. Yang, Xin & Cao, Wenjie & Wang, Kai & Yin, Haodong & Wu, Jianjun & Wu, Lingxiao, 2025. "Integrated scheduling of truck and drone fleets for cargo transportation in post-disaster relief: A two-stage stochastic optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 196(C).
    2. Amirsahami, Amirali & Barzinpour, Farnaz & Pishvaee, Mir Saman, 2025. "A fuzzy programming model for decentralization and drone utilization in urban humanitarian relief chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
    3. Peng, Wenhao & Wang, Dujuan & Yin, Yunqiang & Cheng, T.C.E., 2025. "Multi-agent deep reinforcement learning-based truck-drone collaborative routing with dynamic emergency response," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
    4. 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).
    5. Deng, Menghua & Li, Yuanbo & Ding, Jianpeng & Zhou, Yanlin & Zhang, Lianming, 2024. "Stochastic and robust truck-and-drone routing problems with deadlines: A Benders decomposition approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 190(C).
    6. Yichen Lu & Chao Yang & Jun Yang, 2022. "A multi-objective humanitarian pickup and delivery vehicle routing problem with drones," Annals of Operations Research, Springer, vol. 319(1), pages 291-353, December.
    7. Ghoniem, Ahmed & Boz, Semih & El-Adle, Amro M., 2025. "Parcel delivery by vehicle and drone in ordered customer neighborhoods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    8. Dudu Guo & Yinuo Su & Xiaojiang Zhang & Zhen Yang & Pengbin Duan, 2024. "Multi-Objective Optimization of Short-Inverted Transport Scheduling Strategy Based on Road–Railway Intermodal Transport," Sustainability, MDPI, vol. 16(15), pages 1-25, July.
    9. 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.
    10. He, Xinyu & He, Fang & Li, Lishuai & Zhang, Lei & Xiao, Gang, 2022. "A route network planning method for urban air delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    11. Chen, Enming & Zhou, Zhongbao & Li, Ruiyang & Chang, Zhongxiang & Shi, Jianmai, 2024. "The multi-fleet delivery problem combined with trucks, tricycles, and drones for last-mile logistics efficiency requirements under multiple budget constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 187(C).
    12. 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).
    13. Wang, Xiaohan & Chen, Xiqun (Michael) & Xie, Chi & Cheong, Taesu, 2024. "Coordinative dispatching of shared and public transportation under passenger flow outburst," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    14. 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).
    15. Ruiqi Xiao & Min Xiao & Hanbin Xiao & Ze Zhu, 2025. "Optimizing Port Seafood Logistics Paths: A Multi-Objective Approach for Zero-Carbon and Congestion Management," Sustainability, MDPI, vol. 17(5), pages 1-28, March.
    16. 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).
    17. Wei, Yuanhan & Wang, Yong & Hu, Xiangpei, 2025. "The two-echelon truck-unmanned ground vehicle routing problem with time-dependent travel times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    18. 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).
    19. Sun, Peng & Zhao, Dongpan & Chen, Qingxin & Yu, Xinyao & Zhu, Ning, 2025. "Distributionally robust optimization for pre-disaster facility location problem with 3D printing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    20. Zhen, Lu & Gao, Jiajing & Tan, Zheyi & Laporte, Gilbert & Baldacci, Roberto, 2023. "Territorial design for customers with demand frequency," European Journal of Operational Research, Elsevier, vol. 309(1), pages 82-101.

    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:transe:v:197:y:2025:i:c:s1366554525001061. 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/600244/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.