GAN Based Data Analysis and Mining for Smart Shop Floor Scheduling

Mining knowledge from data is an important way to solve scheduling problems. Adequate samples are a prerequisite for mining effective scheduling knowledge. However, it is difficult and time-consuming to obtain high-quality samples from the production process. To solve this problem, we propose a data mining method for smart shop floor scheduling based on GAN (Generative Adversarial Network). First, GAN is used to learn the distribution of initial samples and generate enough simulation samples to meet the data requirements for scheduling knowledge mining. Then, SVR (Support Vector Regression) is applied to perform scheduling knowledge mining, that is to establish the mapping relationship between shop floor production state and optimal scheduling strategy. The proposed method has been validated on the production system model MiniFab. Compared with the traditional sample acquisition method, the proposed method can effectively shorten the sample acquisition time and ensure the validity of the mined scheduling knowledge.