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

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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.
Publisher
PALGRAVE MACMILLAN LTD
Issue Date
2017-02
Language
English
Article Type
Article
Keywords

SPACE-BASED AIS; PREDICTION; ARRIVALS

Citation

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, v.68, no.2, pp.182 - 191

ISSN
0160-5682
DOI
10.1057/s41274-016-0104-4
URI
http://hdl.handle.net/10203/223281
Appears in Collection
IE-Journal Papers(저널논문)
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