Resumen
Port state control (PSC) plays an important role in enhancing maritime safety and protecting the marine environment. Since the inspection resources are limited and the inspection process is costly and time-consuming, a critical issue for port states to guarantee inspection efficiency is to accurately select ships with a high risk for inspection. To address this issue, this study proposes three prediction models to predict the ship deficiency number and a ship selection optimization model based on the prediction results to target the riskiest ships for inspection. In addition to a linear regression model for ship deficiency number prediction solved by the least squares method, we establish two prediction models with the pairwise-comparison target based semi-?smart predict then optimize? (semi-SPO) method. Specifically, a linear programming model and a support vector machine (SVM) model are built and both have a loss function to minimize the sum of predicted ranking errors of each pair of ships regarding their deficiency numbers. We use the Hong Kong port as a case study, which shows that the SVM model based on the semi-SPO approach performs best among the three models with the least computation time and best ship selection decisions.