Reducing computation time in simulation-based optimization of manufacturing systems

The analysis of production systems using discrete, event-based simulation is wide spread and generally accepted as a decision support technology. It aims either at the comparison of competitive system designs or the identification of a best possible parameter configuration of a simulation model. Here, combinatorial techniques of simulation and optimization methods support the user in finding optimal solutions, but typically result in long computation times, which often prohibits a practical application in industry. To close this gap, this paper presents a fast converging procedure combining a Genetic Algorithm with a material flow simulation including an interactive analysis of simulation runs. An early termination of simulation runs is used for unpromising parameter configurations. The integrated implementation allows automated, distributed simulation runs for practical, complex production systems. A use-case shows the proof of concept with a reference model and demonstrates the resulting speed-up of this approach.