IDEAS home Printed from https://ideas.repec.org/p/cor/louvrp/3270.html

Deep Reinforcement Learning for Inventory Optimization with Non-Stationary Uncertain Demand

Author

Listed:
  • Dehaybe, Henri

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Catanzaro, Daniele

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Chevalier, Philippe

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

Abstract

We consider here a single-item lot sizing problem with fixed costs, lead time, and both backorders and lost sales, and we show that, after an appropriate training in randomly generated environments, Deep Reinforcement Learning (DRL) agents can interpolate in real-time near-optimal dynamic policies on instances with a rolling-horizon, provided a previously unseen demand forecast and without the need to periodically resolve the problem. Extensive computational experiments show that the policies provided by these agents compete, and in some circumstances even outperform by several percentage points of gap, those provided by heuristics based on dynamic programming. These results confirm the importance of DRL in the context of inventory control problems and support its use in solving practical instances featuring realistic assumptions.

Suggested Citation

  • Dehaybe, Henri & Catanzaro, Daniele & Chevalier, Philippe, 2023. "Deep Reinforcement Learning for Inventory Optimization with Non-Stationary Uncertain Demand," LIDAM Reprints CORE 3270, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:3270
    DOI: https://doi.org/10.1016/j.ejor.2023.10.007
    Note: In: European Journal of Operational Research, 2023
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. Cui, Geng & Imura, Naoto & Nishinari, Katsuhiro & Ezaki, Takahiro, 2025. "On order smoothing interpolating the order-up-to and constant order policies," Omega, Elsevier, vol. 136(C).
    3. Sarkar, Puja & Khanapuri, Vivekanand B. & Tiwari, Manoj Kumar, 2025. "Integration of prediction and optimization for smart stock portfolio selection," European Journal of Operational Research, Elsevier, vol. 321(1), pages 243-256.
    4. Sara Cheraghi & Abdorrahman Haeri & Seyed Farid Ghannadpour, 2025. "A dynamic and intelligent decision-making framework for a platelet inventory-distribution network," Operational Research, Springer, vol. 25(3), pages 1-61, September.
    5. Temizöz, Tarkan & Imdahl, Christina & Dijkman, Remco & Lamghari-Idrissi, Douniel & van Jaarsveld, Willem, 2025. "Deep Controlled Learning for Inventory Control," European Journal of Operational Research, Elsevier, vol. 324(1), pages 104-117.
    6. 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).
    7. Abada, Ibrahim & Lambin, Xavier & Tchakarov, Nikolay, 2024. "Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits?," European Journal of Operational Research, Elsevier, vol. 318(3), pages 927-953.
    8. Bo Zhang & Wen Jun Tan & Wentong Cai & Allan N. Zhang, 2024. "Leveraging Multi-Agent Reinforcement Learning for Digital Transformation in Supply Chain Inventory Optimization," Sustainability, MDPI, vol. 16(22), pages 1-17, November.
    9. Akkerman, Fabian & Prak, Dennis & Mes, Martijn, 2025. "Dynamic reordering and inspection for the multi-item Inventory Record Inaccuracy problem," European Journal of Operational Research, Elsevier, vol. 321(2), pages 428-444.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cor:louvrp:3270. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alain GILLIS (email available below). General contact details of provider: https://edirc.repec.org/data/coreebe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.