Context: Due to an increase in inter-terminal transportation (ITT) volume within a container port; complexity of transportation processes between the terminals has also increased. Problems with the current way of handling ITT resources are expected to rise in the near future. Different types of vehicles are already in place for transporting containers between different terminals in a container port. However, there need to be an efficient and effective use of these vehicle types in order to get maximum benefits out of these resources.

Objectives: In this thesis, we investigate and propose a solution model for ITT considering the combination of both manned (MTS, Trucks) and unmanned (AGV) vehicles. An agent based model is proposed for ITT focusing on three ITT vehicle types. Objective of proposed model is to investigate the capabilities and combination of different vehicles for transporting containers between different container terminals in a port. 

Methods: A systematic literature review is conducted to identify the problems and methods and approaches for solving those problems in the domain of container transportation. As a case, an agent-based model is proposed for the Maasvlakte area of the Rotterdam port. Simulations are performed on different scenarios to compare three different road vehicle types, i.e., AGV, MTS, and truck, in a network comprising of ten terminals.

Results: Literature review results indicate that heuristics is the most commonly used method to solve different problems of container transportation in the recent past. The review also depicts that limited research has been published focusing on ITT when compared to intra-terminal transportation. Simulation results of our proposed model indicate that AGVs outperforms trucks in terms of loading/unloading time and number of vehicles required to handle the given volume of all scenarios. In most of the cases, it is observed that the number of trucks required are twice as much as compared to AGVs in order to transport containers between different terminals. Results also show that lower number MTS vehicles (as compared to AGVs) are required for handling containers in certain scenarios; however, the loading/unloading time for MTS is much higher than that of AGVs.

Conclusions: Using agent-based simulation experiments, we propose a model that can help in estimating the required resources (vehicles) to handle the ITT containers volume and improve the utilization of different resources in a network of terminals. From comparison of three road vehicle types, it was concluded that trucks are incapable to handle higher container volume in an ITT. It was also concluded that AGVs can be an appropriate choice if automated operations are supported in the terminals, otherwise MTS is the best choice concerning the number of vehicles required to handle containers. Our simulation results may help the ITT planners in better estimations and planning of ITT to meet current and future challenges of transporting high containers volume.