A Dynamically Optimized Fluctuation Smoothing Rule for Scheduling Jobs in a Wafer Fabrication Factory

2012 ◽  
pp. 265-284
Author(s):  
Toly Chen
Keyword(s):  
Standard Deviation ◽  
Test Data ◽  
Cycle Time ◽  
Performance Metrics ◽  
Experimental Results ◽  
The Other ◽  
Wafer Fabrication ◽  
Fluctuation Smoothing ◽  
Time Standard ◽  
Nonlinear Fluctuation

This paper presents a dynamically optimized fluctuation smoothing rule to improve the performance of scheduling jobs in a wafer fabrication factory. The rule has been modified from the four-factor bi-criteria nonlinear fluctuation smoothing (4f-biNFS) rule, by dynamically adjusting factors. Some properties of the dynamically optimized fluctuation smoothing rule were also discussed theoretically. In addition, production simulation was also applied to generate some test data for evaluating the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology was better than some existing approaches to reduce the average cycle time and cycle time standard deviation. The results also showed that it was possible to improve the performance of one without sacrificing the other performance metrics.

A Dynamically Optimized Fluctuation Smoothing Rule for Scheduling Jobs in a Wafer Fabrication Factory

2011 ◽  
Vol 7 (4) ◽  
pp. 47-64 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Standard Deviation ◽  
Test Data ◽  
Cycle Time ◽  
Performance Metrics ◽  
The Other ◽  
Wafer Fabrication ◽  
Fluctuation Smoothing ◽  
Time Standard ◽  
Nonlinear Fluctuation ◽  
Better Than

This paper presents a dynamically optimized fluctuation smoothing rule to improve the performance of scheduling jobs in a wafer fabrication factory. The rule has been modified from the four-factor bi-criteria nonlinear fluctuation smoothing (4f-biNFS) rule, by dynamically adjusting factors. Some properties of the dynamically optimized fluctuation smoothing rule were also discussed theoretically. In addition, production simulation was also applied to generate some test data for evaluating the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology was better than some existing approaches to reduce the average cycle time and cycle time standard deviation. The results also showed that it was possible to improve the performance of one without sacrificing the other performance metrics.

scholarly journals Enhancing Scheduling Performance for a Wafer Fabrication Factory: The Biobjective Slack-Diversifying Nonlinear Fluctuation-Smoothing Rule

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Toly Chen ◽  
Yu Cheng Wang
Keyword(s):  
Standard Deviation ◽  
Wafer Fabrication ◽  
The Future ◽  
Simulated Case ◽  
Fluctuation Smoothing ◽  
Nonlinear Fluctuation

A biobjective slack-diversifying nonlinear fluctuation-smoothing rule (biSDNFS) is proposed in the present work to improve the scheduling performance of a wafer fabrication factory. This rule was derived from a one-factor bi-objective nonlinear fluctuation-smoothing rule (1f-biNFS) by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several previous studies. The efficacy of the biSDNFS was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.

A Fuzzy Fluctuation Smoothing Rule for Job Dispatching in a Wafer Fabrication Factory

2012 ◽  
Vol 2 (4) ◽  
pp. 47-63 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Simulation Study ◽  
Cycle Time ◽  
Production System ◽  
Wafer Fabrication ◽  
Dispatching Rules ◽  
Cycle Times ◽  
Job Dispatching ◽  
Fluctuation Smoothing ◽  
Mean Cycle ◽  
Nonlinear Fluctuation

Job dispatching in a wafer fabrication factory is a difficult task, mainly due to the complexity of the production system and the uncertainty involved in the production activities. Recently, a number of advanced dispatching rules were proposed, which estimate the remaining cycle times of jobs. This predictive nature is conducive to the effectiveness of these rules. If the uncertainty in the remaining cycle time can be better considered, incorrect scheduling will possibly be reduced. The tailored nonlinear fluctuation smoothing rule for mean cycle time (TNFSMCT) is fuzzified in this study, by expressing the remaining cycle time with a fuzzy value. The effectiveness of the proposed methodology is illustrated with a simulation study.

scholarly journals A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Toly Chen ◽  
Yi-Chi Wang
Keyword(s):  
Cycle Time ◽  
Back Propagation ◽  
Fuzzy Rule ◽  
Wafer Fabrication ◽  
Job Dispatching ◽  
Fuzzy Back Propagation Network ◽  
Fluctuation Smoothing ◽  
Propagation Network ◽  
Time Standard ◽  
Tardy Jobs

This paper proposes a fuzzy slack-diversifying fluctuation-smoothing rule to enhance the scheduling performance in a wafer fabrication factory. The proposed rule considers the uncertainty in the remaining cycle time and is aimed at simultaneous improvement of the average cycle time, cycle time standard deviation, the maximum lateness, and number of tardy jobs. Existing publications rarely discusse ways to optimize all of these at the same time. An important input to the proposed rule is the job remaining cycle time. To this end, this paper proposes a self-adjusted fuzzy back propagation network (SA-FBPN) approach to estimate the remaining cycle time of a job. In addition, a systematic procedure is also established, which can solve the problem of slack overlapping in a nonsubjective way and optimize the overall scheduling performance. The simulation study provides evidence that the proposed rule can improve the four performance measures simultaneously.

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 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

scholarly journals A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Toly Chen ◽  
Richard Romanowski
Keyword(s):  
Soft Computing ◽  
Cycle Time ◽  
Fuzzy Classification ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Soft Computing Techniques ◽  
Fuzzy Neural ◽  
Job Dispatching ◽  
Neural Network Prediction ◽  
Fluctuation Smoothing

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future.

A FUZZY-NEURAL FLUCTUATION SMOOTHING RULE FOR SCHEDULING JOBS WITH VARIOUS PRIORITIES IN A SEMICONDUCTOR MANUFACTURING FACTORY

2009 ◽  
Vol 17 (03) ◽  
pp. 397-417 ◽  
Author(s):  
TOLY CHEN ◽  
YU-CHENG LIN
Keyword(s):  
Cycle Time ◽  
Semiconductor Manufacturing ◽  
Optimal Solution ◽  
Time Estimation ◽  
Pareto Optimal Solution ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
Fluctuation Smoothing

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.

Job remaining cycle time estimation with a post-classifying fuzzy-neural approach in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1021-1031 ◽  
Author(s):  
T Chen
Keyword(s):  
Test Data ◽  
Simulation Study ◽  
Cycle Time ◽  
Estimation Error ◽  
Back Propagation ◽  
Time Estimation ◽  
Wafer Fabrication ◽  
Fuzzy Neural ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

A post-classifying fuzzy-neural approach is proposed in this study for estimating the remaining cycle time of each job in a wafer fabrication plant, which has seldom been investigated in past studies but is a critical task for the wafer fabrication plant. In the methodology proposed, the fuzzy back-propagation network (FBPN) approach for job cycle time estimation is modified with the proportional adjustment approach to estimate the remaining cycle time instead. Besides, unlike existing cycle time estimation approaches, in the methodology proposed a job is not preclassified but rather post-classified after the estimation error has been generated. For this purpose, a back-propagation network is used as the post-classification algorithm. To evaluate the effectiveness of the methodology proposed, production simulation is used in this study to generate some test data. According to experimental results, the accuracy of estimating the remaining cycle time could be improved by up to 64 per cent with the proposed methodology.

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