  The Vehicle Routing Problem (VRP) is one of the most intensively studied
combinatorial optimisation problems for which numerous models and algorithms
have been proposed. To tackle the complexities, uncertainties and dynamics
involved in real-world VRP applications, Machine Learning (ML) methods have
been used in combination with analytical approaches to enhance problem
formulations and algorithmic performance across different problem solving
scenarios. However, the relevant papers are scattered in several traditional
research fields with very different, sometimes confusing, terminologies. This
paper presents a first, comprehensive review of hybrid methods that combine
analytical techniques with ML tools in addressing VRP problems. Specifically,
we review the emerging research streams on ML-assisted VRP modelling and
ML-assisted VRP optimisation. We conclude that ML can be beneficial in
enhancing VRP modelling, and improving the performance of algorithms for both
online and offline VRP optimisations. Finally, challenges and future
opportunities of VRP research are discussed.
