A Fuzzy-Neural Fluctuation Smoothing Rule for Scheduling Jobs with Various Priorities in a semiconductor Manufacturing Factory

A fuzzy-neural fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs with various priorities in a semiconductor manufacturing factory. The fuzzy-neural fluctuation smoothing rule is modified from the well-known fluctuation smoothing rule by improving the accuracy of estimating the remaining cycle time of a job, which is done by applying Chen's fuzzy-neural approach with multiple buckets. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study. According to experimental results, incorporating a more accurate remaining cycle time estimation mechanism did improve the scheduling performance especially in reducing the average cycle times. Besides, the fuzzy-neural fluctuation smoothing rule was also shown to be a Pareto optimal solution for scheduling jobs with various priorities in a semiconductor manufacturing factory.

[1]  Pei-Chann Chang,et al.  A Case-Based Reasoning Approach for Due-Date Assignment in a Wafer Fabrication Factory , 2001, ICCBR.

[2]  John W. Fowler,et al.  Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling , 2003 .

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

[4]  John W. Fowler,et al.  A combined dispatching criteria approach to scheduling semiconductor manufacturing systems , 2001 .

[5]  Toly Chen,et al.  A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab , 2006, Neural Processing Letters.

[6]  Appa Iyer Sivakumar,et al.  Job shop scheduling techniques in semiconductor manufacturing , 2006 .

[7]  N. Zerhouni,et al.  A genetic algorithm and data mining based meta-heuristic for job shop scheduling problem , 2002, IEEE International Conference on Systems, Man and Cybernetics.

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

[9]  John W. Fowler,et al.  A multi-criteria approach for scheduling semiconductor wafer fabrication facilities , 2008, J. Sched..

[10]  David A. Koonce,et al.  Using data mining to find patterns in genetic algorithm solutions to a job shop schedule , 2000 .

[11]  Jih-Chang Hsieh,et al.  A neural networks approach for due-date assignment in a wafer fabrication factory , 2003 .

[12]  Weiming Shen,et al.  A Multiagent-Based Decision-Making System for Semiconductor Wafer Fabrication With Hard Temporal Constraints , 2008, IEEE Transactions on Semiconductor Manufacturing.

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

[14]  Pei-Chann Chang,et al.  Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory , 2005, J. Intell. Manuf..

[15]  Yi-Feng Hung,et al.  DISPATCHING RULES USING FLOW TIME PREDICTIONS FOR SEMICONDUCTOR WAFER FABRICATIONS , 2002 .

[16]  Lawrence M. Wein,et al.  Scheduling semiconductor wafer fabrication , 1988 .

[17]  S. Barman.  The impact of priority rule combinations on lateness and tardiness , 1998 .

[18]  Kevin J. Dooley,et al.  Dynamic rules for due-date assignment , 1991 .

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

[20]  Toly Chen.  A fuzzy back propagation network for output time prediction in a wafer fab , 2003, Appl. Soft Comput..