Design Parameter Optimization of Automated Production Systems

Modern automated production systems need to be efficient as well as flexible. While the state-of-the-art commercial control software enables customization and provides flexibility for production systems, finding efficient control parameters is still realized in an ad-hoc way, e.g., trial-and-error. In this paper, we propose to apply simulation optimization techniques to efficiently search the optimal control parameters. We use the ordinal transformation and optimal sampling methods to efficiently search control parameters under uncertainty. A case study is reported.