Data-driven and multi-scale modeling approach for production system simulation (Fundamental study on model identification for single process systems) / 生産システムシミュレーションにおけるデータ駆動型マルチスケールモデリングアプローチの提案(単工程システムを対象としたモデル同定に関する基礎検討)
- Resource Type
- Journal Article
- Authors
- Daisuke KOKURYO; Nobutada FUJII; Satoshi NAGAHARA; Toshiya KAIHARA; 國領 大介; 永原 聡士; 藤井 信忠; 貝原 俊也
- Source
- 日本機械学会論文集 / Transactions of the JSME (in Japanese). 2023, 89(928):23-00205
- Subject
- Machine learning
Production system
Production system simulation
System identification
- Language
- Japanese
- ISSN
- 2187-9761
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.