  Inventory Routing Problem (IRP) is a crucial challenge in supply chain
management as it involves optimizing efficient route selection while
considering the uncertainty of inventory demand planning. To solve IRPs,
usually a two-stage approach is employed, where demand is predicted using
machine learning techniques first, and then an optimization algorithm is used
to minimize routing costs. Our experiment shows machine learning models fall
short of achieving perfect accuracy because inventory levels are influenced by
the dynamic business environment, which, in turn, affects the optimization
problem in the next stage, resulting in sub-optimal decisions. In this paper,
we formulate and propose a decision-focused learning-based approach to solving
real-world IRPs. This approach directly integrates inventory prediction and
routing optimization within an end-to-end system potentially ensuring a robust
supply chain strategy.
