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Early detection of vessel delays using combined historical and real-time information

Author

Listed:
  • Sungil Kim

    (Ulsan National Institute of Science and Technology (UNIST))

  • Heeyoung Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Yongro Park

    (Data Analytics Lab, R&D Center, Samsung SDS)

Abstract

In ocean transportation, detecting vessel delays in advance or in real time is important for fourth-party logistics (4PL) in order to fulfill the expectations of customers and to help customers reduce delay costs. However, the early detection of vessel delays faces the challenges of numerous uncertainties, including weather conditions, port congestion, booking issues, and route selection. Recently, 4PLs have adopted advanced tracking technologies such as satellite-based automatic identification systems (S-AISs) that produce a vast amount of real-time vessel tracking information, thus providing new opportunities to enhance the early detection of vessel delays. This paper proposes a data-driven method for the early detection of vessel delays: in our new framework of refined case-based reasoning (CBR), real-time S-AIS vessel tracking data are utilized in combination with historical shipping data. The proposed method also provides a process of analyzing the causes of delays by matching the tracking patterns of real-time shipments with those of historical shipping data. Real data examples from a logistics company demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Sungil Kim & Heeyoung Kim & Yongro Park, 2017. "Early detection of vessel delays using combined historical and real-time information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(2), pages 182-191, February.
    • Handle: RePEc:pal:jorsoc:v:68:y:2017:i:2:d:10.1057_s41274-016-0104-4
    DOI: 10.1057/s41274-016-0104-4
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    References listed on IDEAS

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    1. Gianfranco Fancello & Claudia Pani & Marco Pisano & Patrizia Serra & Paola Zuddas & Paolo Fadda, 2011. "Prediction of arrival times and human resources allocation for container terminal," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 13(2), pages 142-173, June.
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    Cited by:

    1. Pierluigi Zerbino & Davide Aloini & Riccardo Dulmin & Valeria Mininno, 2019. "Towards Analytics-Enabled Efficiency Improvements in Maritime Transportation: A Case Study in a Mediterranean Port," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    2. Sara El Mekkaoui & Loubna Benabbou & Abdelaziz Berrado, 2023. "Deep learning models for vessel’s ETA prediction: bulk ports perspective," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 5-28, March.

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