A fuzzy-neural approach for optimizing the performance of job dispatching in a wafer fabrication factory

A fuzzy-neural approach is presented in this study to optimize the performance of job dispatching in a wafer fabrication factory. The traditional optimization methods in this field have a few problems. To tackle these problems, we performed several treatments. First, we applied a more effective fuzzy-neural approach to estimate the remaining cycle time of a job. Then we established a systematic procedure to determine the optimal values of the parameters in the two-factor tailored nonlinear fluctuation smoothing rule for the mean cycle time, in order to optimize the scheduling performance. To assess the effectiveness of the proposed methodology, we conducted a production simulation. According to the experimental results, the proposed methodology is better than the existing approaches in optimizing the average cycle time.

[1]  Chun-Hung Chen,et al.  Scheduling semiconductor wafer fabrication by using ordinal optimization-based simulation , 2001, IEEE Trans. Robotics Autom..

[2]  Toly Chen,et al.  A Fuzzy-Neural Fluctuation Smoothing Rule for Scheduling Jobs with Various Priorities in a semiconductor Manufacturing Factory , 2009, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  T Chen.  Dynamic fuzzy-neural fluctuation smoothing rule for jobs scheduling in a wafer fabrication factory , 2009 .

[4]  Toly Chen,et al.  Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach with multiple buckets and partial normalization , 2009 .

[5]  Zhibin Jiang,et al.  Simulation-based optimization of dispatching rules for semiconductor wafer fabrication system scheduling by the response surface methodology , 2009 .

[6]  Toly Chen,et al.  A bi-criteria nonlinear fluctuation smoothing rule incorporating the SOM–FBPN remaining cycle time estimator for scheduling a wafer fab—a simulation study , 2010 .

[7]  Kamal Pal,et al.  Soft computing methods used for the modelling and optimisation of Gas Metal Arc Welding: a review , 2011, Int. J. Manuf. Res..

[8]  T Chen.  Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study , 2009 .

[9]  Toly Chen.  Optimized fuzzy-neuro system for scheduling wafer fabrication , 2009 .

[10]  Hsin-Chieh Wu,et al.  A fuzzy-neural approach for remaining cycle time estimation in a semiconductor manufacturing factory-a simulation study , 2009 .

[11]  K. Altendorfer,et al.  A new dispatching rule for optimizing machine utilization at a semiconductor test field , 2007, 2007 IEEE/SEMI Advanced Semiconductor Manufacturing Conference.

[12]  Toly Chen,et al.  A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory—a simulation study , 2009 .

[13]  S.C.H. Lu,et al.  Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants , 1994 .

[14]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Toly Chen,et al.  An intelligent hybrid system for wafer lot output time prediction , 2007, Adv. Eng. Informatics.

[16]  Tin-Chih Toly Chen,et al.  A fuzzy-neural system for scheduling a wafer fabrication factory , 2010 .

[17]  Chia-Nan Wang,et al.  A simulated model for cycle time reduction by acquiring optimal lot size in semiconductor manufacturing , 2007 .

[18]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[19]  Tin-Chih Toly Chen,et al.  A BI-Criteria four-factor fluctuation smoothing rule for scheduling jobs in a wafer fabrication factory , 2010 .

[20]  Toly Chen,et al.  An optimized tailored nonlinear fluctuation smoothing rule for scheduling a semiconductor manufacturing factory , 2010, Comput. Ind. Eng..

[21]  Toly Chen,et al.  Incorporating the FCM-BPN approach with nonlinear programming for internal due date assignment in a wafer fabrication plant , 2010 .