There is no doubt about the need of optimizing the factor of time in today's production. Several optimization methods are in use. The optimization is often based on a single objective, e.g. the makespan. In practice, more than one time-related objectives are to be optimized. We focus on simulation based optimization of manufacturing processes. Therefore, control strategies are searched using several metaheuristic methods like Simulated Annealing, Genetic Algorithms, or Tabu Search and evaluated by running a simulation of the modeled manufacturing system repeatedly. Best results are proposed to be scheduled. The simulation yields in a big amount of data which is reduced to several time-related objectives like machine utilization, due date keeping, lead time, cycle time and others. Of course, these objectives have different dimensions and are contradictory in some cases. This makes the goal setting process very difficult. Several objectives have to be combined to a fitness value, which is used for optimization. Algorithms for combining the objectives are user-dependent, preferences need to be set, and interdependencies between different goals need to be detected. We present methods for interactive goal setting, dynamic objective observation, filtering of interdependencies between objectives and finding of fitness functions with consideration of user preferences. The normalization of different objectives allows the comparison between various dimensions and the detection of correlation between indifferent objectives. Relations between objectives are shown in correlation diagrams. Only competitive goals are used for the fitness and graphically presented during the optimization process (Petal Diagram and Modified Star Graph). Sometimes the hardest part of optimization can be solved using these methods. The basic theories will be shown and explained with the results of some optimized scheduling problems of our industry partners.
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