The Automated Guided Vehicle (AGV) is the most commonly used piece of horizontal movement that uses electric power from an internal battery pack. With much focus on the charging strategy, current charge operation models lack the ability to model large scale terminal operations and to incorporate new types of horizontal movement. The objective of this research is to show the impact of recharge operations on terminal performances. A discrete event simulation model is based on the characteristics of APMT MVII to model the current impact. The results of numerical experiments show charging operations have a limited impact on the productivity of Lift-AGV. An exception is the change in battery type. Using a Li-ion battery lowers the need for recharging and increases total productivity. The findings show the importance of integrating modelling to accurately project terminal operations.

Quay cranes on modern container terminals have the possibility to lift two 40ft. containers side by side. In order to be able to perform this type of lift, the crane must be equipped with a tandem spreader. As this type of spreader cannot be used for performing certain lifts, such as the handling of hatch covers, switches between single spreader and tandem spreader are inevitable. However, there is no method in current literature nor in practice to determine when the spreader changes should be performed. Therefore, the aim of this research is to develop a model that calculates the optimal moments to switch spreaders. As the performance of tandem lifting depends on the cycle times, a cycle time prediction model is developed first. This Artificial Neural Network model predicts the cycle time based on the type of lift, the actual position of the container(s) on the ship, the weight of the load and the current wind speed. The predictions yield a Mean Absolute Percentage Error of less than 11%. Afterwards, the cycle time prediction model is used to develop an optimal spreader switching strategy model in the form of a Mixed Integer Linear Programming model. A case study, in which several different bay layouts of a container ship need to be handled, shows a decrease in handling time of up to 22% compared to the traditional spreader switching strategy.