IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v6y2025i2d10.1007_s43069-025-00449-6.html
   My bibliography  Save this article

Operations Research, Machine Learning, and Integrated Techniques for Decision Problems in the Seaside Area of Container Terminals

Author

Listed:
  • Haoqi Xie

    (University of Genova
    University of Genova)

  • Daniela Ambrosino

    (University of Genova
    University of Genova)

Abstract

Container terminal plays a crucial role in the supply chain facilitating the modal exchange from maritime transport to other modes of transportation. The seaside area along with its logistic processes related to the ship arrival and the unloading/loading operations has been significantly impacted by both the phenomenon of naval gigantism and technological innovations. To face the new challenges arising in this context, novel techniques have been proposed in the recent literature. This paper provides an overview of current trends in addressing problems in the seaside area, with a particular focus on operational research, machine learning, and their integration as promising tools for supporting decision-makers. The proposed literature review is based on papers published between 2014 and 2023 in scientific journals from two leading publishers, Springer and Elsevier. A new classification schema is presented to analyze better the current state and the trend in operational research and machine learning approaches to solve problems. Additionally, data analysis is conducted to provide further insights. The paper concludes with a discussion of potential research directions, highlighting the opportunities for integrating these approaches to enhance decision-making and address emerging challenges in container terminal operations.

Suggested Citation

  • Haoqi Xie & Daniela Ambrosino, 2025. "Operations Research, Machine Learning, and Integrated Techniques for Decision Problems in the Seaside Area of Container Terminals," SN Operations Research Forum, Springer, vol. 6(2), pages 1-51, June.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00449-6
    DOI: 10.1007/s43069-025-00449-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-025-00449-6
    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/s43069-025-00449-6?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:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00449-6. 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.