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Graph attention reinforcement learning with flexible matching policies for multi-depot vehicle routing problems

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  • Zhang, Ke
  • Lin, Xi
  • Li, Meng

Abstract

Multi-depot vehicle routing problem with soft time windows (MD-VRPSTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive problems instantly. Thus, this paper presents a novel reinforcement learning algorithm integrated with graph attention network (GAT-RL) to efficiently solve the problem. This method utilizes the encoder–decoder architecture to produce routes for vehicles starting from different depots iteratively. The encoder architecture employs graph attention network to mine the complex spatial–temporal correlations within time windows. Then, the decoder architecture designs fixed-order and full-pair matching policies to generate solutions. After off-line training, experiments show that this approach consistently outperforms Google OR-Tools with negligible computational time. Particularly, the robustness of the pre-trained model is validated under multiple sources of variations and uncertainties, including customer/depot numbers, vehicle capacities, and en-route traffic conditions.

Suggested Citation

  • Zhang, Ke & Lin, Xi & Li, Meng, 2023. "Graph attention reinforcement learning with flexible matching policies for multi-depot vehicle routing problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    • Handle: RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437123000067
    DOI: 10.1016/j.physa.2023.128451
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    References listed on IDEAS

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    1. Yang, Yun & Ma, Changxi & Ling, Gang, 2022. "Pre-location for temporary distribution station of urban emergency materials considering priority under COVID-19: A case study of Wuhan City, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    2. Dong, Hanxuan & Ding, Fan & Tan, Huachun & Zhang, Hailong, 2022. "Laplacian integration of graph convolutional network with tensor completion for traffic prediction with missing data in inter-city highway network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    3. I D Giosa & I L Tansini & I O Viera, 2002. "New assignment algorithms for the multi-depot vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 977-984, September.
    4. Zhang, Kunpeng & Feng, Xiaoliang & Jia, Ning & Zhao, Liang & He, Zhengbing, 2022. "TSR-GAN: Generative Adversarial Networks for Traffic State Reconstruction with Time Space Diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    5. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    6. Wang, Jun & Wang, Wenjun & Liu, Xueli & Yu, Wei & Li, Xiaoming & Sun, Peiliang, 2022. "Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    7. J-F Cordeau & G Laporte & A Mercier, 2001. "A unified tabu search heuristic for vehicle routing problems with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(8), pages 928-936, August.
    8. Li, Xue-yan & Li, Xue-mei & Yang, Lingrun & Li, Jing, 2018. "Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 77-92.
    9. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
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