Deep Learning for Perishable Inventory Systems with Human Knowledge

• URL: http://arxiv.org/abs/2601.15589v1
• Date: Thu, 22 Jan 2026 02:26:32 GMT
• Title: Deep Learning for Perishable Inventory Systems with Human Knowledge
• Authors: Xuan Liao, Zhenkang Peng, Ying Rong,
• Abstract summary: We study a perishable inventory system with random lead times in which both the demand process and the lead time distribution are unknown.<n>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.<n>We develop two end-to-end variants: a purely blackbox approach that outputs order quantities directly (E2E-BB) and a structure-guided approach that embeds the projected inventory level (PIL) policy.
• Score: 0.6920276126310231
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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