IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v339y2024i1d10.1007_s10479-024-05927-4.html
   My bibliography  Save this article

Optimizing inland container shipping through reinforcement learning

Author

Listed:
  • Vid Tomljenovic

    (Tilburg University)

  • Yasemin Merzifonluoglu

    (Tilburg University)

  • Giacomo Spigler

    (Tilburg University)

Abstract

In this study, we investigate the container delivery problem and explore ways to optimize the complex and nuanced system of inland container shipping. Our aim is to fulfill customer demand while maximizing customer service and minimizing logistics costs. To address the challenges posed by an unpredictable and rapidly-evolving environment, we examine the potential of leveraging reinforcement learning (RL) to automate the decision-making process and craft agile, efficient delivery schedules. Through a rigorous and comprehensive numerical study, we evaluate the efficacy of this approach by comparing the performance of several high-performance heuristic policies with that of agents trained using reinforcement learning, under various problem settings. Our results demonstrate that a reinforcement learning approach is robust and particularly useful for decision makers who must match logistics demand with capacity dynamically and have multiple objectives.

Suggested Citation

  • Vid Tomljenovic & Yasemin Merzifonluoglu & Giacomo Spigler, 2024. "Optimizing inland container shipping through reinforcement learning," Annals of Operations Research, Springer, vol. 339(1), pages 1025-1050, August.
    • Handle: RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-024-05927-4
    DOI: 10.1007/s10479-024-05927-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-024-05927-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-024-05927-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-024-05927-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.