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Scalable deep reinforcement learning in the non-stationary capacitated lot sizing problem

Author

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  • van Hezewijk, Lotte
  • Dellaert, Nico P.
  • van Jaarsveld, Willem L.

Abstract

Capacitated lot sizing problems in situations with stationary and non-stationary demand (SCLSP) are very common in practice. Solving problems with a large number of items using Deep Reinforcement Learning (DRL) is challenging due to the large action space. This paper proposes a new Markov Decision Process (MDP) formulation to solve this problem, by decomposing the production quantity decisions in a period into sub-decisions, which reduces the action space dramatically. We demonstrate that applying Deep Controlled Learning (DCL) yields policies that outperform the benchmark heuristic as well as a prior DRL implementation. By using the decomposed MDP formulation and DCL method outlined in this paper, we can solve larger problems compared to the previous DRL implementation. Moreover, we adopt a non-stationary demand model for training the policy, which enables us to readily apply the trained policy in dynamic environments when demand changes.

Suggested Citation

  • van Hezewijk, Lotte & Dellaert, Nico P. & van Jaarsveld, Willem L., 2025. "Scalable deep reinforcement learning in the non-stationary capacitated lot sizing problem," International Journal of Production Economics, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:proeco:v:284:y:2025:i:c:s0925527325000866
    DOI: 10.1016/j.ijpe.2025.109601
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