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Development of Two Highly-Efficient and Innovative Inspection Schemes for PSC Inspection

Author

Listed:
  • Ran Yan

    (Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China)

  • Dan Zhuge

    (Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China)

  • Shuaian Wang

    (Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China)

Abstract

Port state control (PSC) inspection contributes a lot to improving maritime safety and protecting the marine environment. After selecting the ships coming to a port for inspection, one critical challenge faced by the PSC authorities is deciding what deficiency items should be inspected and what the inspection sequence of these items is. To address this problem, two innovative and high-efficient PSC inspection schemes describing specific PSC inspection items and sequence are proposed 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 with inspecting ships with no less than five deficiency items and limited inspection resources.

Suggested Citation

  • Ran Yan & Dan Zhuge & Shuaian Wang, 2021. "Development of Two Highly-Efficient and Innovative Inspection Schemes for PSC Inspection," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(03), pages 1-23, June.
  • Handle: RePEc:wsi:apjorx:v:38:y:2021:i:03:n:s0217595920400138
    DOI: 10.1142/S0217595920400138
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    Cited by:

    1. 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.
    2. 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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