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Association rule learning to improve deficiency inspection in port state control

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  • Wu-Hsun Chung
  • Sheng-Long Kao
  • Chun-Min Chang
  • Chien-Chung Yuan

Abstract

The inspection of foreign ships in national ports is a critical measure in port state control (PSC), preventing substandard ships from entering national ports. Multifarious inspection items, limited inspection time and inspector manpower are challenging PSC inspection. This research applies data mining to analyze historical PSC inspection records in Taiwan’s major ports to extract potential valuable information for PSC onboard inspections. Using the Apriori Algorithm, the analysis identifies many useful association rules among PSC deficiencies in terms of specific ship characteristics, such as ship types, societies, and flags. The general rules identified show that the items ‘Water/Weathertight conditions’ and ‘Fire safety’ are significantly related. Besides, in the analysis of the various ship types, several different rules are found. After comparing the analysis of ship types and ship societies, it can be observed that the association rules for specific ship types, such as oil tankers, have a better effect than those for individual ship societies do. These identified rules can not only help inspectors effectively spot the associated deficiencies, but also improve the efficiency of PSC inspection. The ports other than Taiwan’s ports can apply a similar analysis method to identify corresponding association rules suitable for their own inspections.

Suggested Citation

  • Wu-Hsun Chung & Sheng-Long Kao & Chun-Min Chang & Chien-Chung Yuan, 2020. "Association rule learning to improve deficiency inspection in port state control," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(3), pages 332-351, April.
    • Handle: RePEc:taf:marpmg:v:47:y:2020:i:3:p:332-351
    DOI: 10.1080/03088839.2019.1688877
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    Cited by:

    1. Tian, Xuecheng & Yan, Ran & Liu, Yannick & Wang, Shuaian, 2023. "A smart predict-then-optimize method for targeted and cost-effective maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 32-52.
    2. Yan, Ran & Wang, Shuaian & Cao, Jiannong & Sun, Defeng, 2021. "Shipping Domain Knowledge Informed Prediction and Optimization in Port State Control," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 52-78.
    3. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "A Decision-Focused Learning Framework for Vessel Selection Problem," Mathematics, MDPI, vol. 11(16), pages 1-13, August.
    4. Zhu, Jiang-Hong & Yang, Qiang & Jiang, Jun, 2023. "Identifying crucial deficiency categories influencing ship detention: A method of combining cloud model and prospect theory," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Xuecheng Tian & Shuaian Wang, 2022. "Cost-Sensitive Laplacian Logistic Regression for Ship Detention Prediction," Mathematics, MDPI, vol. 11(1), pages 1-15, December.

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