A Novel Predictive Control Based Framework for Optimizing Intermodal Container Terminal Operations

Abstract

Due to the increase in world-wide containerized cargo transport port authorities are facing considerable pressure to increase efficiency of existing facilities. Container vessels with 18,000 TEUs (twenty-foot equivalent units) are expected soon to create high flow peaks at container terminals. In this paper we propose a new framework for managing intermodal container terminals, based on the model predictive control methodology. A model based on queues and container categorization is used by a model predictive controller to solve the handling resource allocation problem in a container terminal in an optimal way, while respecting constraints on resource availability. The optimization of the operations is performed in an integrated way for the whole terminal rather than only for an individual subprocess. Containers are categorized into empty and full containers, and divided in classes according to their final destination. With more detailed information available, like container final destination, it is possible to establish priorities for the container flows inside the terminal. The order in which the container classes should be loaded into a carrier can now be addressed taking into account the carrier future route. The model ability to track the number of containers per class makes this framework suitable for describing terminals integrated in an intermodal transport network and a valuable tool for coordinating the transport modal shift towards a more sustainable and reliable transport. The potential of the proposed framework is illustrated with simulation studies based on a high-peak flow scenario and for a long-term scheduled scenario.