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

Cited 5 time in webofscience Cited 0 time in scopus
  • Hit : 296
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Sungilko
dc.contributor.authorKim, Heeyoungko
dc.contributor.authorPark, Yongroko
dc.date.accessioned2017-04-17T07:28:59Z-
dc.date.available2017-04-17T07:28:59Z-
dc.date.created2017-04-10-
dc.date.created2017-04-10-
dc.date.issued2017-02-
dc.identifier.citationJOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, v.68, no.2, pp.182 - 191-
dc.identifier.issn0160-5682-
dc.identifier.urihttp://hdl.handle.net/10203/223281-
dc.description.abstractIn 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.-
dc.languageEnglish-
dc.publisherPALGRAVE MACMILLAN LTD-
dc.subjectSPACE-BASED AIS-
dc.subjectPREDICTION-
dc.subjectARRIVALS-
dc.titleEarly detection of vessel delays using combined historical and real-time information-
dc.typeArticle-
dc.identifier.wosid000394519900006-
dc.identifier.scopusid2-s2.0-85021425150-
dc.type.rimsART-
dc.citation.volume68-
dc.citation.issue2-
dc.citation.beginningpage182-
dc.citation.endingpage191-
dc.citation.publicationnameJOURNAL OF THE OPERATIONAL RESEARCH SOCIETY-
dc.identifier.doi10.1057/s41274-016-0104-4-
dc.contributor.localauthorKim, Heeyoung-
dc.contributor.nonIdAuthorKim, Sungil-
dc.contributor.nonIdAuthorPark, Yongro-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorbig data-
dc.subject.keywordAuthorpredictive analytics-
dc.subject.keywordAuthorcase-based reasoning-
dc.subject.keywordAuthordata stream-
dc.subject.keywordAuthordelay detection-
dc.subject.keywordAuthorreal-time analytics-
dc.subject.keywordPlusSPACE-BASED AIS-
dc.subject.keywordPlusPREDICTION-
dc.subject.keywordPlusARRIVALS-
Appears in Collection
IE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 5 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0