USING AGGREGATED DISCRETE EVENT SIMULATION MODELS AND MULTI-OBJECTIVE OPTIMIZATION TO IMPROVE REAL-WORLD FACTORIES

Improving production line performance and identifying bottlenecks using simulation-based optimization has been shown to be an effective approach. Nevertheless, for larger production systems which are consisted of multiple production lines, using simulation-based optimization can be too computationally expensive, due to the complexity of the models. Previous research has shown promising techniques for aggregating production line data into computationally efficient modules, which enables the simulation of higher-level systems, i.e., factories. This paper shows how a real-world factory flow can be optimized by applying the previously mentioned aggregation techniques in combination with multi-objective optimization using an experimental approach. The particular case studied in this paper reveals potential reductions of storage levels by over 30%, lead time reductions by 67%, and batch sizes reduced by more than 50% while maintaining the delivery precision of the industrial system.