  Learning to solve combinatorial optimization problems, such as the vehicle
routing problem, offers great computational advantages over classical
operations research solvers and heuristics. The recently developed deep
reinforcement learning approaches either improve an initially given solution
iteratively or sequentially construct a set of individual tours. However, most
of the existing learning-based approaches are not able to work for a fixed
number of vehicles and thus bypass the complex assignment problem of the
customers onto an apriori given number of available vehicles. On the other
hand, this makes them less suitable for real applications, as many logistic
service providers rely on solutions provided for a specific bounded fleet size
and cannot accommodate short term changes to the number of vehicles. In
contrast we propose a powerful supervised deep learning framework that
constructs a complete tour plan from scratch while respecting an apriori fixed
number of available vehicles. In combination with an efficient post-processing
scheme, our supervised approach is not only much faster and easier to train but
also achieves competitive results that incorporate the practical aspect of
vehicle costs. In thorough controlled experiments we compare our method to
multiple state-of-the-art approaches where we demonstrate stable performance,
while utilizing less vehicles and shed some light on existent inconsistencies
in the experimentation protocols of the related work.
