Yard Template Planning in a Transshipment Hub: Gaussian Process Regression

A yard template in a container terminal assigns subblocks for containers with the same departing vessel to reduce vessel turnaround time with the decreased number of container rehandling. Because vehicle congestion can significantly affect the vessel turnaround time, a terminal operator carefully determines the yard template considering the complex traffic congestion on the entire container terminal. In this study, we propose an application of a Gaussian Process (GP) to predict the vessel turnaround time under the impacts of vehicle interruption and blocking. Based on the predictions, we determine the yard template with the shortest predicted vessel turnaround time among candidate yard templates. Through simulation experiments, we compare the proposed approach and a baseline model based on a Mixed Integer Programming (MIP). The simulation results show that the application reduces the vessel turnaround time by 6.66% compared with the baseline model.