  We investigate a learning decision support system for vehicle routing, where
the routing engine learns implicit preferences that human planners have when
manually creating route plans (or routings). The goal is to use these learned
subjective preferences on top of the distance-based objective criterion in
vehicle routing systems. This is an alternative to the practice of
distinctively formulating a custom VRP for every company with its own routing
requirements. Instead, we assume the presence of past vehicle routing solutions
over similar sets of customers, and learn to make similar choices. The learning
approach is based on the concept of learning a Markov model, which corresponds
to a probabilistic transition matrix, rather than a deterministic distance
matrix. This nevertheless allows us to use existing arc routing VRP software in
creating the actual routings, and to optimize over both distances and
preferences at the same time. For the learning, we explore different schemes to
construct the probabilistic transition matrix that can co-evolve with changing
preferences over time. Our results on a use-case with a small transportation
company show that our method is able to generate results that are close to the
manually created solutions, without needing to characterize all constraints and
sub-objectives explicitly. Even in the case of changes in the customer sets,
our method is able to find solutions that are closer to the actual routings
than when using only distances, and hence, solutions that require fewer manual
changes when transformed into practical routings.
