IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v68y2017i2d10.1057_s41274-016-0104-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41274-016-0104-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41274-016-0104-4?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.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    2. Yun Peng & Xiangda Li & Wenyuan Wang & Ke Liu & Xiao Bing & Xiangqun Song, 2018. "A Method for Determining the Required Power Capacity of an On-Shore Power System Considering Uncertainties of Arriving Ships," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
    3. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    4. Patrizia Serra & Paolo Fadda & Gianfranco Fancello, 2016. "Evaluation of alternative scenarios of labour flexibility for dockworkers in maritime container terminals," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(3), pages 371-385, April.
    5. Massimo Di Francesco & Gianfranco Fancello & Patrizia Serra & Paola Zuddas, 2015. "Optimal management of human resources in transhipment container ports," Maritime Policy & Management, Taylor & Francis Journals, vol. 42(2), pages 127-144, February.
    6. Scheidweiler, Tina & Jahn, Carlos, 2019. "Business analytics on AIS data: Potentials, limitations and perspectives," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 342-368, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. 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.
    8. Albert Veenstra & Rogier Harmelink, 2021. "On the quality of ship arrival predictions," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(4), pages 655-673, December.
    9. Lorenz Kolley & Nicolas Rückert & Marvin Kastner & Carlos Jahn & Kathrin Fischer, 2023. "Robust berth scheduling using machine learning for vessel arrival time prediction," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 29-69, March.

    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:pal:jorsoc:v:68:y:2017:i:2:d:10.1057_s41274-016-0104-4. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.palgrave-journals.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.