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Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision

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  • Yang, Zhisen
  • Wan, Chengpeng
  • Yang, Zaili
  • Yu, Qing

Abstract

Port State Control (PSC) inspections have been implemented as an administrative measure to detect and detain substandard ships and thus to ensure maritime safety. Advanced risk models were developed to investigate the impact of factors influencing ship detention. Although showing much attractiveness, current studies still reveal a key challenge on how such analysis can improve the ship performance in PSC inspections and aid PSC detention risk control decision. By incorporating a data-driven Bayesian network (BN) into the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, this paper proposes a new ship detention risk control methodology, in which the decision criteria are generated from the root risk variables, and the alternatives refer to the established strategies adopted by ship-owners in their practical ship detention risk control. Along with the new methodology, the main technical novelty of this paper lies in the quantitative measurement of the effectiveness of each strategy in terms of the reduction of detention rate in a dynamic manner. Its practical contributions are seen, from both ship owner and port authority perspectives, through the provisions of useful insights on dynamic evaluation of rational control strategies to reduce ship detention risk under various PSC inspection scenarios.

Suggested Citation

  • Yang, Zhisen & Wan, Chengpeng & Yang, Zaili & Yu, Qing, 2021. "Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021003094
    DOI: 10.1016/j.ress.2021.107784
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    Cited by:

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    3. Antão, P. & Sun, S. & Teixeira, A.P. & Guedes Soares, C., 2023. "Quantitative assessment of ship collision risk influencing factors from worldwide accident and fleet data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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    5. Liu, Kezhong & Yu, Qing & Yang, Zhisen & Wan, Chengpeng & Yang, Zaili, 2022. "BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. Yan, Ran & Wang, Shuaian & Zhen, Lu, 2023. "An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    7. Yang, Zhisen & Yu, Qing & Yang, Zaili & Wan, Chengpeng, 2024. "A data-driven Bayesian model for evaluating the duration of detention of ships in PSC inspections," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    8. 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).
    9. Yan, Ran & Mo, Haoyu & Guo, Xiaomeng & Yang, Ying & Wang, Shuaian, 2022. "Is port state control influenced by the COVID-19? Evidence from inspection data," Transport Policy, Elsevier, vol. 123(C), pages 82-103.

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