  We introduce a combinatorial optimization-enriched machine learning pipeline
and a novel learning paradigm to solve inventory routing problems with
stochastic demand and dynamic inventory updates. After each inventory update,
our approach reduces replenishment and routing decisions to an optimal solution
of a capacitated prize-collecting traveling salesman problem for which
well-established algorithms exist. Discovering good prize parametrizations is
non-trivial; therefore, we have developed a machine learning approach. We
evaluate the performance of our pipeline in settings with steady-state and more
complex demand patterns. Compared to previous works, the policy generated by
our algorithm leads to significant cost savings, achieves lower inference time,
and can even leverage contextual information.
