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

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  • 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
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