Solving vehicle routing problems (VRPs) is an essential task for many industrial applications. While VRPs have been traditionally studied in the operations research (OR) domain, they have lately been the subject of extensive work in the machine learning (ML) community. Both the OR and ML communities have begun to integrate ML into their methods, but in vastly different ways. While the OR community mostly relies on simplistic ML methods, the ML community generally uses deep learning, but fails to outperform OR baselines. To address this gap, this competition, a joint effort of several previous competitions, brings together the OR and ML communities to solve a challenging VRP variant on real-world data provided by ORTEC, a leading provider of vehicle routing software. The challenge focuses on both a `classic' deterministic VRP with time windows (VRPTW) and a dynamic version in which new orders arrive over the course of a day. As a baseline, we will provide a state-of-the-art VRPTW solver and a simple strategy to use it to solve the dynamic variant, thus ensuring that all competition participants have the tools necessary to solve both versions of the problem. We anticipate that the winning method will significantly advance the state-of-the-art for solving routing problems, therefore providing a strong foundation for further research in both the OR and ML communities, as well as a practical impact on the real-world solving of VRPs.