Daniel Christopher Merten

• The following thesis addresses several shortcomings in the academic literature surrounding steel production planning. These shortcomings are summarized as (i) failure to incorporate tacit production knowledge into steel decision support systems, (ii) negligence of physical boundaries / limitations of the steel manufacturing process, and (iii) lack of global (as opposed to local) optimization approaches in steel scheduling algorithms. Not incorporating tacit production knowledge typically leads to wrong decision advice and sub-optimal decision-making, whereas neglecting the theoretical output limits of the steel production process can cause unnecessary machine breakdowns and diminished productivity; also, if production scheduling algorithms focus too much on specific sub-elements of the optimization problem (e.g. upstream production lines), this will adverse effects on the remaining elements (e.g. downstream production lines). To overcome these shortcomings, different methodologies are applied: (i) Through a mixture of association rules mining and complex network analysis, we extract production knowledge hidden in historical production data that documents the decisions of a human expert planner; this extracted knowledge could now be utilized by decision support systems. (ii) In historical production data, we identify a theoretical upper limit of the casting speed that mitigates the productivity of continuous casters; adapting the average casting speed to this limit could increase the production output. This phenomenon is further investigated through minimal models of production systems which are characterized by (a) stochastic production inputs and (b) disruptive thresholds on the production output. (iii) We develop two genetic algorithms out of which the first algorithm optimizes multiple production sequences at the same time as opposed to one after another, while the second algorithm simultaneously generates schedules for two production processes.