Predictive control of container flows in maritime intermodal terminals

Predictive control is investigated as a paradigm for the allocation of handling resources to transfer containers inside intermodal terminals. The decisions on the allocation of such resources are derived from the minimization of performance cost functions that measure the lay times of carriers over a forward horizon basing on a model of the container flows. Such a model allows one to take advantage of the information available in real time on the arrival or departure of carriers with the corresponding amounts of containers scheduled for loading or unloading. The resulting strategy of resource allocation can be regarded as a feedback control law and is obtained by solving nonlinear programming problems online. Since the computation may be too expensive, a technique based on the idea of approximating offline such a law is proposed. The approximation is performed by using neural networks, which allow one to construct an approximate feedback controller and generate the corresponding online control actions with a negligible computational burden. The effectiveness of the approach is shown via simulations in a case study.