Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Logistics and Maritime Studies | en_US |
| dc.contributor.advisor | Wang, Shuaian Hans (LMS) | en_US |
| dc.creator | Yan, Ran | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/10643 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Development of machine learning and data mining models for port state control inspection | en_US |
| dcterms.abstract | This thesis aims to address two critical issues faced in port state control (PSC) inspection in maritime transportation by using machine learning and data mining models: ship selection for inspection before conducting PSC inspections and deciding onboard inspection sequence during PSC inspections. In this first study, a data-driven Bayesian network classifier named Tree Augmented Naive Bayes (TAN) classifier is developed to identify high-risk foreign vessels coming to the port state authorities. By using data of 250 PSC inspection records from Hong Kong port in 2017, we construct the structure and quantitative parts of the TAN classifier. Then the proposed classifier is validated by another 50 PSC inspection records from the same port. The results show that, compared with the Ship Risk Profile selection scheme that is currently implemented in practice, the TAN classifier can discover 130% more deficiencies on average. Several analyses of the variables (features) included in the model are also conducted. The proposed classifier can help the PSC authorities to better identify substandard ships as well as to allocate inspection resources. The second study proposes two innovative and highly-efficient PSC inspection schemes describing specific PSC inspection sequences for the inspectors' reference when time and resources are limited, especially when there are difficulties in estimating the possible deficiencies in advance. Both schemes take the occurrence probability, inspection cost, and ignoring loss of each deficiency item into account. More specifically, the first inspection scheme is based on the occurrence probabilities of the deficiency items in the whole data set, while the second scheme further considers the correlations among the deficiency items extracted by association rules. The results of numerical experiments show that the efficiency of the two proposed inspection schemes is 1.5 times higher than that of the currently used inspection scheme. In addition, the second inspection scheme performs better than the first inspection scheme, especially when inspecting ships with no less than 5 deficiency items using limited inspection resources. | en_US |
| dcterms.extent | ix, 90 pages : illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2020 | en_US |
| dcterms.educationalLevel | M.Phil. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Ships -- Inspection | en_US |
| dcterms.LCSH | Ships -- Inspection -- Data processing | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Data mining | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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