標題: | Due date assignment using artificial neural networks under different shop floor control strategies |
作者: | Hsu, SY Sha, DY 工業工程與管理學系 Department of Industrial Engineering and Management |
公開日期: | 1-五月-2004 |
摘要: | 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. |
URI: | http://dx.doi.org/10.1080/00207540310001624375 http://hdl.handle.net/11536/26804 |
ISSN: | 0020-7543 |
DOI: | 10.1080/00207540310001624375 |
期刊: | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH |
Volume: | 42 |
Issue: | 9 |
起始頁: | 1727 |
結束頁: | 1745 |
顯示於類別: | 期刊論文 |