標題: Due-date assignment in wafer fabrication using artificial neural networks
作者: Sha, DY
Hsu, SY
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: due-date assignment;artificial neural network;wafer fabrication;simulation;shop floor control
公開日期: 1-May-2004
摘要: Due-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined. From the simulation and statistical results, ANN-based DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others.
URI: http://hdl.handle.net/11536/26821
ISSN: 0268-3768
期刊: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume: 23
Issue: 9-10
起始頁: 768
結束頁: 775
Appears in Collections:Articles


Files in This Item:

  1. 000221468200019.pdf