  We study the problem of learning the preferences of drivers and planners in
the context of last mile delivery. Given a data set containing historical
decisions and delivery locations, the goal is to capture the implicit
preferences of the decision-makers. We consider two ways to use the historical
data: one is through a probability estimation method that learns transition
probabilities between stops (or zones). This is a fast and accurate method,
recently studied in a VRP setting. Furthermore, we explore the use of machine
learning to infer how to best balance multiple objectives such as distance,
probability and penalties. Specifically, we cast the learning problem as a
structured output prediction problem, where training is done by repeatedly
calling the TSP solver. Another important aspect we consider is that for
last-mile delivery, every address is a potential client and hence the data is
very sparse. Hence, we propose a two-stage approach that first learns
preferences at the zone level in order to compute a zone routing; after which a
penalty-based TSP computes the stop routing. Results show that the zone
transition probability estimation performs well, and that the structured output
prediction learning can improve the results further. We hence showcase a
successful combination of both probability estimation and machine learning, all
the while using standard TSP solvers, both during learning and to compute the
final solution; this means the methodology is applicable to other, real-life,
TSP variants, or proprietary solvers.
