With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same day deliveries. Existing multi-stage stochastic optimization approaches that allow to solve the underlying dynamic vehicle routing problem are either computationally too expensive for an application in online settings, or -- in the case of reinforcement learning -- struggle to perform well on high-dimensional combinatorial problems. To mitigate these drawbacks, we propose a novel machine learning pipeline that incorporates a combinatorial optimization layer. We apply this general pipeline to a dynamic vehicle routing problem with dispatching waves, which was recently promoted in the EURO Meets NeurIPS Vehicle Routing Competition at NeurIPS 2022. Our methodology ranked first in this competition, outperforming all other approaches in solving the proposed dynamic vehicle routing problem. With this work, we provide a comprehensive numerical study that further highlights the efficacy and benefits of the proposed pipeline beyond the results achieved in the competition, e.g., by showcasing the robustness of the encoded policy against unseen instances and scenarios.

翻译：随着电子商务的兴起和客户需求的日益增长，物流服务提供商在日常规划中面临新的复杂性，主要源于当日配送的高效处理需求。现有的多层随机优化方法虽能求解潜在的动态车辆路径问题，但在在线场景中计算成本过高，或——以强化学习为例——难以在高维组合问题上取得良好表现。为缓解上述缺陷，我们提出了一种融合组合优化层的新型机器学习流程。我们将这一通用流程应用于近期在NeurIPS 2022举办的EURO Meets NeurIPS车辆路径竞赛中推广的含波次调度的动态车辆路径问题。在本竞赛中，我们的方法排名第一，在求解所提出的动态车辆路径问题时优于所有其他方法。本文通过全面的数值研究进一步验证了所提流程的有效性与优势，除竞赛成果外，还展示了编码策略对未见实例与场景的鲁棒性。