Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2607.22356v1
• Date: Fri, 24 Jul 2026 14:37:02 GMT
• Title: Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning
• Abstract summary: We propose an integrated optimization framework which couples a learned routing with real-time dispatching oracles.<n>Experiments on real-world datasets from Cainiao Logistics are used to test the performance of our approach.
• Score: 3.9921873121588103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms. This challenge is fundamentally driven by two key and tightly coupled decision-making processes: order dispatching and routing. Solving them separately overlooks their interdependence, while fully end-to-end learning can be unstable and costly on large, variable-scale instances due to sparse rewards. To solve this problem, we propose an integrated optimization framework which couples a learned routing oracle with real-time dispatching heuristics. For the routing subproblem, we develop a Dynamic-Residual Graph Attention Network encoder with a Look-Ahead Courier-Personalized decoder. For the dispatching subproblem, we develop a routing-oracle-guided dispatching heuristic with local search, where the oracle provides near-optimal solutions to select candidate couriers while retaining real-time scalability. Extensive experiments on real-world datasets from Cainiao Logistics are used to test the performance of our approach, including an offline evaluation and an online rolling-horizon simulation. The experimental results show that our approach outperforms other benchmarks regarding solution quality and solving time, indicating it can effectively support logistics companies in solving real-time and large-scale last-mile pickup problems.

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