Paper was Accepted Advanced Engineering Informatics!!!

As global supply chains grow increasingly complex and cargo volumes continue to rise, the accurate prediction of vessel turnaround time (VTT) has become essential for enhancing port operational efficiency. Previous studies primarily utilized static features, such as vessel specifications and container volumes, often failing to capture the dynamic nature of real-world operations. To address this limitation, we propose a forecasting framework that incorporates dynamic operation indicators (OIs) from a two-stage queuing model into time-series models. Leveraging operational data from Busan Port, our approach achieves of up to 28% improvement in mean absolute percentage error compared with existing techniques. Explainability analysis using Shapley additive explanations (SHAP) further demonstrates that OIs rank among the most influential features. By providing more reliable VTT predictions, port operators can enhance berth planning, optimize resource allocation, minimize scheduling delays, and enhance overall port efficiency. These results highlight the practical value of the framework for data-driven decision-making in port operations.