The competitiveness of container seaport terminals is mainly determined by the time in port for ships (transshipment time), and the rates for loading and discharging. The goal of this thesis is to reduce transshipment times by using model predictive scheduling, combined with a switching max-plus linear system description.Container terminals generally utilize cranes to load and unload ships, specialized equipment to store containers in large stacks, and vehicles to transport containers between cranes and stacks. An unloading cycle generally consists of three distinct steps, which are the unloading by a quay crane, the transport from the crane to the stack, and finally the stacking. The dynamics of this transportation system as a whole can be influenced by choosing the vehicle which is assigned to each of the container jobs, and by choosing the order in which vehicles and stacking cranes handle their containers. The dynamics of this system are mainly determined by the time it takes to perform each of these steps, and by synchronizations between the steps. This makes it possible to describe this system with a switching max-plus linear (SMPL) model. This thesis will show how one can describe the container transport system as an SMPL system. Furthermore, this thesis will show how the goal of minimizing transshipment times can be reached with the use of model predictive scheduling (MPS). It will be shown that the MPS problem for this SMPL system can be rewritten as a mixed integer linear programming (MILP) problem, for which various efficient solvers exist. This allows one to find the solution to the MPS problem for large scale container terminals within a short solution time.ii