  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.
