Hybrid model predictive control for equipment in an automated container terminal

Over the last decades, there has been a significant growth of global freight transport due to the enormous commercial trade. Over 60% of worldwide deep-sea cargo is transported by containers. The increased amount of containers that arrive and depart with container ships provides much pressure for terminal operators. The throughput, i.e., the number of containers handled per hour, should be increased. A container terminal is characterized by a large number of pieces of equipment that operate in a dynamically changing environment. The transport of a container depends on the actions of multiple pieces of equipment that are physically spread all over the container terminal. We are investigating how to effectively manage the volume growth by considering a more integrated way of looking at transport of freight. In particular in this paper, we propose to use the hybrid automaton modeling framework for modeling the handling of containers. Model predictive control is proposed for achieving the desired performance.