DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorYan, Ren_US
dc.creatorWang, Sen_US
dc.creatorCao, Jen_US
dc.creatorSun, Den_US
dc.date.accessioned2021-08-13T06:13:40Z-
dc.date.available2021-08-13T06:13:40Z-
dc.identifier.issn0191-2615en_US
dc.identifier.urihttp://hdl.handle.net/10397/90667-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectDomain knowledge informed artificial intelligenceen_US
dc.subjectDomain knowledge informed machine learningen_US
dc.subjectInspection templateen_US
dc.subjectOptimization in port state control (PSC)en_US
dc.subjectPSCO scheduling modelen_US
dc.titleShipping domain knowledge informed prediction and optimization in port state controlen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage52en_US
dc.identifier.epage78en_US
dc.identifier.volume149en_US
dc.identifier.doi10.1016/j.trb.2021.05.003en_US
dcterms.abstractMaritime transportation is the backbone of global supply chain. To improve maritime safety, protect the marine environment, and set out seafarers’ rights, port state control (PSC) empowers ports to inspect foreign visiting ships to verify them comply with various international conventions. One critical issue faced by the port states is how to optimally allocate the limited inspection resources for inspecting the visiting ships. To address this issue, this study first develops a state-of-the-art XGBoost model to accurately predict ship deficiency number considering ship generic factors, dynamic factors, and inspection historical factors. Particularly, the XGBoost model takes shipping domain knowledge regarding ship flag, recognized organization, and company performance into account to improve model performance and prediction fairness (e.g., for two ships that are different only in their flag performances, the one with a better flag performance should be predicted to have a better condition than the other). Based on the predictions, a PSC officer (PSCO) scheduling model is proposed to help the maritime authorities optimally allocate inspection resources. Considering that a PSCO can inspect at most four ships in a day, we further propose and incorporate the concepts of inspection template and un-dominated inspection template in the optimization models to reduce problem size as well as improve computation efficiency and model flexibility. Numerical experiments show that the proposed PSCO scheduling model with the predictions of XGBoost as the input is more than 20% better than the current inspection scheme at ports regarding the number of deficiencies detected. In addition, the gap between the proposed model and the model under perfect-forecast policy is only about 8% regarding the number of deficiencies detected. Extensive sensitivity experiments show that the proposed PSCO scheduling model has stable performance and is always better than the current model adopted at ports.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTransportation research. Part B, Methodological, Jul. 2021, v. 149, p. 52-78en_US
dcterms.isPartOfTransportation research. Part B, Methodologicalen_US
dcterms.issued2021-07-
dc.identifier.scopus2-s2.0-85107006400-
dc.identifier.eissn1879-2367en_US
dc.description.validate202108 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1003-n16-
dc.identifier.SubFormID2398-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2023.07.31en_US
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