A neural networks approach for due-date assignment in a wafer fabrication factory

The production processes in a wafer fabrication factory are very complicated and time-consuming. This presents a challenging problem to the production planning and scheduling department for the due-date assignment of each order. This research proposes a simulation model to mimic a real wafer fabrication factory and the flowtime of each order is collected for the purpose of due-date assignment. Various influential variables related to the flowtime of each order are identified through regression analysis. Accordingly, a neural network model is established to forecast the due-date of each order. The system is very applicable in the real world and the experimental results show that the proposed approach is very convincing when compared with the traditional approaches. Significance: The results indicate that the neural networks-based approach effectively reduces the prediction error up to 57.80%. This indicates that the proposed approach has a good potential.