A new data-driven production scheduling method based on digital twin for smart shop floors

Abstract

As a mainstream means for solving smart shop floor production scheduling problems, the data-driven scheduling method has gained considerable attention in recent years. However, extant studies have primarily utilized physical shop floor data with limited quantity and quality to train scheduling models, which suffer from the drawbacks of long training time and poor scheduling performance. Therefore, this study proposes a new data-driven scheduling method based on digital twin for smart shop floors, which utilizes the data from physical shop floor and digital shop floor constructed by digital twin to train scheduling models. Specifically, in this method, a model-level data fusion mechanism is designed to achieve the fusion and complementary advantages of these two types of data, thus providing sufficient and high-quality data support for high-precision model training. Additionally, a multi-layer feedforward neural network with a generative adversarial network-based sample expansion mechanism is further integrated to efficiently generate scheduling decisions. Experiments in a semiconductor production shop floor are conducted to confirm the effectiveness of the proposed method.