Data-driven and multi-scale modeling approach for production system simulation (Fundamental study on model identification for single process systems)

Abstract

Production system simulation is a powerful tool to achieve efficient operations in complicated production systems such as high-mix and low-volume production. However, it takes significant efforts and expertise to construct accurate simulation models. In this article, a novel modeling approach called as data-driven and multi-scale modeling is proposed. The proposed approach combines various modeling methods to maximize the simulation accuracy. In order to verify the usefulness of the proposed approach, computational experiments for simple production systems to compare modeling methods are conducted. The experimental results show that the superiority of modeling methods depends on the background knowledge and available information about target production system and the proper use of modeling methods is important to achieve high accuracy.

Keywords

• production system simulation
• production system
• system identification
• machine learning