Vehicle routing by learning from historical solutions
  The goal of this paper is to investigate a decision support system for
vehicle routing, where the routing engine learns from the subjective decisions
that human planners have made in the past, rather than optimizing a
distance-based objective criterion. This is an alternative to the practice of
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 approach is based on
the concept of learning a first-order 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 route plans. For the learning, we explore different schemes
to construct the probabilistic transition matrix. 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 client sets, our method is able to find solutions that are
closer to the actual route plans than when using distances, and hence,
solutions that would require fewer manual changes to transform into the actual
route plan.
