IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p13127-d1230101.html
   My bibliography  Save this article

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Author

Listed:
  • Wenjiao Zai

    (College of Engineering, Sichuan Normal University, Chengdu 610101, China
    These authors contributed equally to this work.)

  • Junjie Wang

    (College of Engineering, Sichuan Normal University, Chengdu 610101, China
    These authors contributed equally to this work.)

  • Guohui Li

    (College of Engineering, Sichuan Normal University, Chengdu 610101, China)

Abstract

Stable material transportation is essential for quickly restoring the power system following a disaster. Drone-based material transportation can bypass ground transportation’s limitations and reduce transit times. However, the current drone flight trajectory distribution optimization model cannot meet the need for mountainous emergency relief material distribution following a disaster. A power emergency material distribution model with priority conditions is proposed in this paper, along with a two-layer dynamic task-solving framework that takes task dynamics into account. This research proposes an algorithm (TD3PSO) that combines the particle swarm algorithm (PSO) updating technique with the double-delay depth deterministic policy gradient algorithm (TD3) algorithm’s capacity to dynamically parameterize. The final task allocation experiment demonstrates that the modified TD3PSO significantly outperforms the conventional algorithm on the Solomon data set, with an improvement of 26.3% on average over the RLPSO algorithm and a 11.0% reduction in the volatility of the solving impact. When solving under realistic circumstances, the solution effect increases by 1.6% to 13.4%, and the redistribution experiment confirms the framework’s efficacy. As a result, the algorithm and architecture suggested in this paper may successfully address the issue of scheduling drones for power emergencies while enhancing transportation efficiency.

Suggested Citation

  • Wenjiao Zai & Junjie Wang & Guohui Li, 2023. "A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm," Sustainability, MDPI, vol. 15(17), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13127-:d:1230101
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/13127/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/13127/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2020. "Drone routing with energy function: Formulation and exact algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 364-387.
    2. Xuming Wang & Jiaqi Zhou & Xiaobing Yu & Xianrui Yu, 2023. "A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model," Sustainability, MDPI, vol. 15(10), pages 1-31, May.
    3. Cong Chen & Yibai Li & Guangqiao Cao & Jinlong Zhang, 2023. "Research on Dynamic Scheduling Model of Plant Protection UAV Based on Levy Simulated Annealing Algorithm," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    4. Shan Li & Honghai Zhang & Zhuolun Li & Hao Liu, 2021. "An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    5. Xinlin Liu & Tian Jing & Linyi Hou, 2023. "An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment," Mathematics, MDPI, vol. 11(10), pages 1-29, May.
    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 Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.
    2. 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).
    3. 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).
    4. 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.
    5. Xia, Yang & Zeng, Wenjia & Zhang, Canrong & Yang, Hai, 2023. "A branch-and-price-and-cut algorithm for the vehicle routing problem with load-dependent drones," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 80-110.
    6. Li, Hongqi & Wang, Feilong & Zhan, Zhuopeng, 2024. "Drone routing problem with swarm synchronization," European Journal of Operational Research, Elsevier, vol. 314(2), pages 477-495.
    7. Zandieh, Fatemeh & Ghannadpour, Seyed Farid & Mazdeh, Mohammad Mahdavi, 2024. "New integrated routing and surveillance model with drones and charging station considerations," European Journal of Operational Research, Elsevier, vol. 313(2), pages 527-547.
    8. Zhou, Hang & Qin, Hu & Cheng, Chun & Rousseau, Louis-Martin, 2023. "An exact algorithm for the two-echelon vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 124-150.
    9. 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.
    10. Yi Li & Min Liu & Dandan Jiang, 2022. "Application of Unmanned Aerial Vehicles in Logistics: A Literature Review," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    11. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development," Post-Print hal-04381308, HAL.
    12. Maria Elena Bruni & Sara Khodaparasti, 2022. "A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    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. Yang, Yu & Yan, Chiwei & Cao, Yufeng & Roberti, Roberto, 2023. "Planning robust drone-truck delivery routes under road traffic uncertainty," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1145-1160.
    15. Hu, Zhangchen & Chen, Heng & Lyons, Eric & Solak, Senay & Zink, Michael, 2024. "Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    16. Kunpeng Wu & Shaofeng Lu & Haoqin Chen & Minling Feng & Zenghao Lu, 2024. "An Energy-Efficient Logistic Drone Routing Method Considering Dynamic Drone Speed and Payload," Sustainability, MDPI, vol. 16(12), pages 1-20, June.
    17. Sun, Xuting & Fang, Minghao & Guo, Shu & Hu, Yue, 2024. "UAV-rider coordinated dispatching for the on-demand delivery service provider," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    18. Johannes Schmidt & Armin Fügenschuh, 2023. "A two-time-level model for mission and flight planning of an inhomogeneous fleet of unmanned aerial vehicles," Computational Optimization and Applications, Springer, vol. 85(1), pages 293-335, May.
    19. Lin, Meiyan & Lin, Shaodan & Ma, Lijun & Zhang, Lianmin, 2022. "The value of the Physical Internet on the meals-on-wheels delivery system," International Journal of Production Economics, Elsevier, vol. 248(C).
    20. Honghai Zhang & Tian Tian & Ouge Feng & Shixin Wu & Gang Zhong, 2023. "Research on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles," Sustainability, MDPI, vol. 15(15), pages 1-17, August.

    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:gam:jsusta:v:15:y:2023:i:17:p:13127-:d:1230101. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.