Solving Multi-Depots Pickup and Delivery Problem Based on Heterogeneous Collaborative Multi-Attention Mechanism

As the complexity of logistics scenarios continues to increase, numerous new challenges have emerged. In multi-depots pickup and delivery path planning (MDPDP) scenario, these challenges include cross-depot resource coordination, spatio-temporal dependencies of pickup-delivery pairs, and generalization across problem scales. To address these challenges, this paper proposes a deep reinforcement learning framework with a Heterogeneous Collaborative Multi-Attention (HCMA) mechanism. The HCMA framework has three key features. 1) HCMA comprises a three-level heterogeneous attention architecture: an inter-depot collaborative attention module for cross-depot resource allocation, a pickup-delivery pairing attention module that explicitly learns sequential and pairwise relationships between task nodes, and a global attention module to capture long-range node dependencies. 2) HCMA dynamically integrates real-time vehicle status with multi-type node features through attention weighting to balance vehicle resource utilization across depots. 3) HCMA adopts multi-start training and instance augmentation strategies to enhance generalization capability and obtain the global optimal solution. Extensive experiments conducted on random datasets, benchmark datasets, and a real-world Shenzhen logistics dataset demonstrate that the proposed HCMA method outperforms state-of-the-art heuristic algorithms and deep reinforcement learning baselines, while exhibiting superior generalization capability. The source code is publicly available at:https://github.com/CIA-SZU/LAQ