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-May-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
Appears in Collections:Articles