  This paper provides a systematic overview of machine learning methods applied
to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been a
great interest from both machine learning and operations research communities
to solve VRPs either by pure learning methods or by combining them with the
traditional hand-crafted heuristics. We present the taxonomy of the studies for
learning paradigms, solution structures, underlying models, and algorithms. We
present in detail the results of the state-of-the-art methods demonstrating
their competitiveness with the traditional methods. The paper outlines the
future research directions to incorporate learning-based solutions to overcome
the challenges of modern transportation systems.
