Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
This addresses a crucial challenge in supply chain management for businesses, but it appears incremental as it builds on existing decision-focused learning concepts applied to a specific domain.
The paper tackles the Inventory Routing Problem by proposing a decision-focused learning approach that integrates inventory prediction and routing optimization into an end-to-end system, aiming to improve supply chain robustness compared to traditional two-stage methods.
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.