In this thesis, research is done on the influence and benefits of an iterative interaction between a scheduler and its subsystems for an updated scheduler which minimizes to a certain cost. This is done by providing a case study of a beer brewery. The scheduler is obtained by using a switching max-plus linear approach. The subsystems will be simulated, estimated and predicted using the system dynamics of the beer brewing case study. The processes discussed in the beer brewing process are mashing, brewing and fermentation. The simulation is done by filling in the system dynamics with addition of noise. The estimation is done by using an extended Kalman filter, and the predictions are done by filling in the system dynamics with the estimated states and no addition of noise. The updating of the scheduler is done by receiving the estimations and predictions of the subsystems and thereafter using model predictive control on the switching max-plus scheduler, also called model predictive scheduling. The results for the case study are shown as a substantiation of the conclusions drawn.
Ultimately, discussions and further research are given for the reliability of the conclusions and extensions on the above summarized research.