Maritime 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.

海上运输是全球供应链的中坚力量。为了提高海上安全，保护海洋环境并阐明海员的权利，港口国控制局（PSC）授权港口检查外国来访船，以确认它们是否符合各种国际公约。港口国面临的一个关键问题是如何最佳地分配有限的检查资源来检查来访的船舶。为了解决这个问题，本研究首先开发了一种最新的XGBoost模型，该模型可以在考虑船舶通用因素，动态因素和检查历史因素的情况下准确预测船舶缺货数量。特别是，XGBoost模型考虑了有关船旗，公认组织和公司绩效的运输领域知识，以提高模型绩效和预测公平性（例如，对于仅在旗帜表现上有所不同的两艘船，应预测旗帜性能更好的一艘船的状况要好于另一艘。在此基础上，提出了PSC官员调度模型，以帮助海事部门优化分配检查资源。考虑到PSCO一天最多可以检查四艘船，我们进一步提出并纳入了以下概念：优化模型中的检验模板和非支配检验模板，以减少问题大小并提高计算效率和模型灵活性。数值实验表明，所建议的PSCO调度模型将XGBoost作为输入，在检测到的缺陷数量方面，比当前的端口检查方案好20％以上。此外，在缺陷检测数量方面，所提出的模型与完美预测策略下的模型之间的差距仅为8％左右。大量的敏感性实验表明，所提出的PSCO调度模型具有稳定的性能，并且总是优于当前在港口采用的模型。