A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems”

To efficiently operate complicated production systems such as mixed-flow production, production system simulation is a promising tool. However, constructing accurate simulation models requires significant effort. In this study, we proposed a data-driven and multi-scale modeling approach that optimizes model configuration by combining multiple modeling methods. In order to verify the usefulness of the proposed approach, we organized the possible model configurations from the perspective of available data from target system and conducted computational experiments for flow shop production systems to compare various model configurations. The experimental results show that the optimal model configuration strongly depends on the complexity of target system and models to be identified, and it is important to divide the model so as to reduce the complexity.