Deep Learning for Perishable Inventory Systems with Human Knowledge

2026-01-22

AI Summary

This study addresses the challenge of perishable inventory management under unknown demand and replenishment lead times with scarce data by proposing an end-to-end deep learning ordering policy that integrates domain knowledge. The approach constructs a unified loss function incorporating historical data, covariates, and system states, while embedding heuristic structures from inventory theory—such as projected inventory levels—directly into the model architecture. This integration significantly reduces model complexity and enhances data efficiency. The proposed E2E-BPIL method consistently outperforms purely black-box models on both synthetic and real-world datasets. Theoretical analysis further demonstrates that embedding structural priors effectively reduces generalization error, thereby improving policy robustness and learning efficiency.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Deep Learning Theory
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Managing perishable products with limited lifetimes is a fundamental challenge in inventory management, as poor ordering decisions can quickly lead to stockouts or excessive waste. We study a perishable inventory system with random lead times in which both the demand process and the lead time distribution are unknown. We consider a practical setting where orders are placed using limited historical data together with observed covariates and current system states. To improve learning efficiency under limited data, we adopt a marginal cost accounting scheme that assigns each order a single lifetime cost and yields a unified loss function for end-to-end learning. This enables training a deep learning-based policy that maps observed covariates and system states directly to order quantities. We develop two end-to-end variants: a purely black-box approach that outputs order quantities directly (E2E-BB), and a structure-guided approach that embeds the projected inventory level (PIL) policy, capturing inventory effects through explicit computation rather than additional learning (E2E-PIL). We further show that the objective induced by E2E-PIL is homogeneous of degree one, enabling a boosting technique from operational data analytics (ODA) that yields an enhanced policy (E2E-BPIL). Experiments on synthetic and real data establish a robust performance ordering: E2E-BB is dominated by E2E-PIL, which is further improved by E2E-BPIL. Using an excess-risk decomposition, we show that embedding heuristic policy structure reduces effective model complexity and improves learning efficiency with only a modest loss of flexibility. More broadly, our results suggest that deep learning-based decision tools are more effective and robust when guided by human knowledge, highlighting the value of integrating advanced analytics with inventory theory.

Problem

• perishable inventory
• limited lifetime
• random lead times
• unknown demand
• inventory management

Innovation

• deep learning
• perishable inventory
• human knowledge integration
• end-to-end learning
• projected inventory level (PIL)