Zhang et al. present a model for automatically moving containers from an inland port to terminals. They predict, in the introduction, that every major port will double or even triple the number of containers it processes by 2020. The automation seems a valid approach to the problem. In their model, they employ fully automated tracks to transport the containers, automated cranes to lift the containers, and a supervisory controller to synchronize all of the automated units in the system.

The centerpiece of the model is the supervisory controller that deals with all the units and the information related to transportation tasks, road geometry, real-time communication and response, and so on. The authors use a Petri net as a design and analysis tool, and demonstrate the overall system working in a safe and efficient manner. They report the results of their simulation and evaluation of the model. Pier G Mega Terminal at the port of Long Beach and Union Pacific’s Intermodal Container Transfer Facility are used as examples of a container terminal and an inland port. They report that their simulation demonstrates that the proposed system can achieve the desired performance with the road characteristics between the terminal and the inland port. The paper also discusses general design considerations for the system and the automated tracks.

Although the system was designed for transporting containers from terminals to inland ports, it can be applied to automate other transportation systems as well. In designing such complex systems, valuable tools (other than Petri nets) from the literature of artificial intelligence may also be applied. We design mostly based on our expectations. However, there are too many uncertainties in the real world today. In modeling a system, we should consider unexpected things in our designs as well.