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Zero-shot generalization in inventory management: Train, then Estimate and Decide

Author

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  • Temizöz, Tarkan
  • Imdahl, Christina
  • Dijkman, Remco
  • Lamghari-Idrissi, Douniel
  • Van Jaarsveld, Willem

Abstract

Deploying deep reinforcement learning (DRL) in real-world inventory management presents challenges, including dynamic environments and uncertain problem parameters, e.g. demand and lead time distributions. These challenges highlight a research gap, suggesting a need for a unifying framework to model and solve sequential decision-making under parameter uncertainty. We address this by exploring an underexplored area of DRL for inventory management: training generally capable agents (GCAs) under zero-shot generalization (ZSG). Here, GCAs are advanced DRL policies designed to handle a broad range of sampled problem instances with diverse inventory challenges. ZSG refers to the ability to successfully apply learned policies to unseen instances with unknown parameters without retraining.

Suggested Citation

  • Temizöz, Tarkan & Imdahl, Christina & Dijkman, Remco & Lamghari-Idrissi, Douniel & Van Jaarsveld, Willem, 2026. "Zero-shot generalization in inventory management: Train, then Estimate and Decide," European Journal of Operational Research, Elsevier, vol. 333(1), pages 153-173.
    • Handle: RePEc:eee:ejores:v:333:y:2026:i:1:p:153-173
    DOI: 10.1016/j.ejor.2025.12.033
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