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.

翻译：库存路径规划（IRP）是供应链管理中的关键挑战，因其需在考虑库存需求规划不确定性的同时，优化高效路径选择。为解决IRPs问题，通常采用两阶段方法：首先利用机器学习技术预测需求，随后通过优化算法最小化路径成本。我们的实验表明，机器学习模型难以实现完美精度——库存水平受动态商业环境影响，进而影响下一阶段的优化问题，导致次优决策。本文提出一种基于决策聚焦学习的解决方案，用于处理实际IRPs问题。该方法将库存预测与路径优化直接整合至端到端系统中，从而确保稳健的供应链策略。