Single-Period Optimal Inventory Control With Substitution: An End-to-End Framework

The paper presents a framework for addressing the single-period inventory control problem with substitutable resources. It introduces an end-to-end approach to determine inventory levels of multiple resource grades to meet stochastic demand at reduced costs. This framework is applicable to various scenarios such as multi-grade resource order decisions, used product procurement, and cloud computing resource allocation. In contrast to traditional Predict-then-Optimize approaches, which suffer from decoupling between demand forecasting and inventory decision-making, the paper leverages neural networks to directly yield inventory control decisions. A re-engineered loss function is proposed to tackle unsupervised learning challenges, demonstrating improved solution quality and faster inference speed compared to conventional Predict-then-Optimize methods through numerical experiments on three datasets with different data distributions. The research enhances decision efficiency and quality in high-frequency decision-making scenarios. Future directions for model improvement are also discussed.