A second goal is defining methods to smartly search the optimization space without (greatly) affecting the quality of the solutions found. These methods may include heuristics to eliminate parts of the optimization space (decision diagrams, branch and bound, dominance rules, partial order reduction) and efficient heuristics or machine learning approaches to find solutions to kickstart the exploration of the optimization space. The developed methods are to be validated using industrial case studies. Production lines involving Canon’s professional printers and (third-party) finishing equipment, like stackers and booklet makers, will serve as one of the industrial cases to demonstrate and evaluate the developed methods. The assignment will (partially) be carried out at the facilities of TNO’s ESI department, which is located at the High Tech Campus in Eindhoven.