Search

In wafer fabrication processes, batch processing accounts for over 30% of the overall processing time. And it’s a trade-off between machine utilization and wafer waiting time. Therefore, batch machines have become one of the constraint resources during wafer fabrication. How to maintain the utilization and reduce the waiting time are important tasks for production control. Plenty of research in the past several years focused on the dispatching rules of batch processing. According to many researchers, look-ahead batch dispatching rules outperform MBS on waiting times and machine utilization. The look-ahead batching rules that have been developed are DBH, NACH, MCR, and DJAH. However, these rules do not take the due-date information of wafers into consideration, and can’t accelerate the wafer’s fabrication that will not be completed before the due-date. This study will develop a due-date oriented look-ahead batching rule, namely LBCR, that considers the due-date and expects to raise delivery rates and reduce the average tardiness. Firstly, this study will modify those batching rules to fit the manufacturing environment of wafer fabrication. There are serial simulation tests on those batching rules under various kinds of factors in terms of environment, including traffic intensity, product numbers and product mix rate. Finally, this study will compare the five batching rules on different performance indicators. After the simulation and statistic analysis undertaken, LBCR does outperform other batch dispatching rules on due-date related performance indicators, such as tardy rate and average tardiness. [ABSTRACT FROM AUTHOR]

Due date assignment is an important task in shop floor control, affecting both timely delivery and customer satisfaction. Due date related performances are impacted by the quality of the due date assignment methods. Among the simple and easy to implement due date assignment methods, the total work content (TWK) method achieves the best performance for tardiness related performance criteria and is most widely used in practice and in study. The performance of the TWK method can be improved if the due date allowance factor k could render a more precise and accurate flowtime estimation of each individual job. In this study, in order to improve the performance of the TWK method, we have presented a model that incorporated a data mining tool - Decision Tree - for mining the knowledge of job scheduling about due date assignment in a dynamic job shop environment, which is represented by IF-THEN rules and is able to adjust an appropriate factor k according to the condition of the shop at the instant of job arrival, thereby reducing the due date prediction errors of the TWK method. Simulation results show that our proposed rule-based TWK due date assignment (RTWK) model is significantly better than its static and dynamic counterparts (i.e., TWK and Dynamic TWK methods). In addition, the RTWK model also extracted comprehensive scheduling knowledge about due date assignment, expressed in the form of IF-THEN rules, allowing production managers to easily understand the principles of due date assignment . [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]