Enhancing Machine Learning for Situation Aware Dispatching Through Generative Adversarial Network Based Synthetic Data Generation

Adapting dispatch rules via machine learning in a complex manufacturing environment has shown overall factory performance in various studies. However, the performance of the machine learning model depends on the training data. Limited data could reduce the prediction accuracy of the machine learning model, thereby negatively influencing the overall factory performance. Addressing this, we generate synthetic data for the lot attributes, simulate it through a discrete event simulator, and use the resulting data to improve the prediction accuracy for the machine learning model. We evaluate three synthetic data generation approaches: Latin Hypercube, Synthetic Minority Oversampling Technique, and Generative Adversarial Networks (GAN), demonstrating GAN suitability for synthetic data generation. To validate our approach, we apply two evaluation processes: Train on Real, Test on Real, and Train on Synthetic, Test on Real, showing the improved predictive accuracy of the machine learning model when trained with synthetic data.