Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge

Neural networks are widely utilized to extract management knowledge from acquired data, but having enough real data is not always possible. In the early stages of dynamic flexible manufacturing system (FMS) environments, only a few data are obtained, and this means that the scheduling knowledge is often unreliable. The purpose of this research is to utilize data expansion techniques for an obtained small data set to improve the accuracy of machine learning for FMS scheduling. This research proposes a mega-trend-diffusion technique to estimate the domain range of a small data set and produce artificial samples for training the modified backpropagation neural network (BPNN). The tool used is the Pythia software. The results of the FMS simulation model indicate that learning accuracy can be significantly improved when the proposed method is applied to a very small data set.