Due date assignment using artificial neural networks under different shop floor control strategies

Abstract

Due date assignment (DDA) is the first important task in shop floor control. Due date-related performance is impacted by the quality of the DDA rules. Assigning order due dates and delivering the goods to the customer on time will enhance customer service and provide a competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN), is adopted to model new due date assignment rules. An ANN-based DDA rule, combined with simulation technology and statistical analysis, is presented. Whether or not the ANN-based DDA rule can outperform the conventional and Reg-based DDA rules taken from the literature is examined. The interactions between the DDA, order review/release (ORR), and dispatching rules significantly impact upon one another, and it is therefore very important to determine a suitable DDA rule for the various combinations of ORR and dispatching rules. From the simulation and statistical results, the ANN-based DDA rules perform better in due date prediction. The ANN-based DDA rules have a smaller tardiness rate than the other rules. ANN-based DDA rules have a better sensitivity and variance. Therefore, if system information is not difficult to obtain, the ANN-based DDA rule can perform a better due date prediction. This paper provides suggestions for DDA rules under various combinations of ORR and dispatching rules. ANN-Sep is suitable for most of these combinations, especially when ORR, workload regulation (WR) and two boundaries (TB), rules are adopted.

Acknowledgement

The authors acknowledges the subvention from National Science Council (NSC) in Taiwan, Project: NSC 91-2213-E-009-113.