Throughput Approximation by Neural Network for Serial Production Lines With High Up/Downtime Variability

Most of the existing studies on analyzing the productivity of serial production lines focus on cases where the coefficient of variation ( <tex-math notation="LaTeX">$CV$</tex-math> ) for both uptime and downtime is less than 1. Hardly any result is available when <tex-math notation="LaTeX">$CV>1$</tex-math> , i.e., uptime and downtime of machines exhibit high variability. The improvement of the production lines with high variable uptime and downtime depends on heuristic trial and error due to the lack of analysis method. This article suggests a neural network that approximates the throughput of serial production lines from machine and buffer parameters. Four neural network architectures (multilayer perceptron, recurrent neural network, long short-term memory (LSTM), and gated recurrent unit) are compared to determine the most effective architecture for the throughput approximation task. Training data are obtained from discrete-event simulations, encompassing a wide range of parameters. The results indicate that the LSTM model outperforms the other architecture considered. Furthermore, we present bottleneck identification and continuous improvement scenarios utilizing the model.