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.

scholarly journals An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1317
Author(s):  
Xin Huang ◽  
Yuanping Zhu ◽  
Shuqin Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Human Action ◽  
Human Motion ◽  
Estimation Accuracy ◽  
Human Motion Analysis ◽  
Critical Feature ◽  
Fuzzy Neural ◽  
Motion Retrieval

Human motion retrieval and analysis is a useful means of activity recognition to 3D human bodies. An efficient method is proposed to estimate human motion by using symmetric joint points and limb features of various limb parts based on regression task. We primarily obtain the 3D coordinates of symmetric joint points based on the located waist and hip points. By introducing three critical feature points on torso and symmetric joint points’ matching on motion video sequences, the 3D coordinates of symmetric joint points and its asymmetric limb features will not be affected by shading and interference of limb on different postures. With the asymmetric limb features of various human parts, a dynamic regulated Fuzzy neural network (DRFNN) is proposed to estimate human motion for different asymmetric postures using learning algorithm of network parameters and weights. Finally, human sequential actions corresponding to different asymmetric postures are presented according to the best retrieval results by DRFNN based on 3D human action database. Experiments show that compared with the traditional adaptive self-organizing fuzzy neural network (SOFNN) model, the proposed algorithm has higher estimation accuracy and better presentation results compared with the existing human motion analysis algorithms.

scholarly journals Production Control Process using Integrated Robust Data Envelopment Analysis and Fuzzy Neural Network

2019 ◽  
Vol 4 (3) ◽  
pp. 580-590 ◽  
Author(s):  
Hadi Gholizadeh ◽  
Hamed Fazlollahtabar
Keyword(s):  
Neural Network ◽  
Data Envelopment Analysis ◽  
Cycle Time ◽  
Measurement Errors ◽  
Fuzzy Neural Network ◽  
Production Control ◽  
Control Process ◽  
Data Envelopment ◽  
Manufacturing Companies ◽  
Fuzzy Neural

Considering the importance of the quality and responsiveness of manufacturing companies to customers, the most important principle can be considered to be the reduction of the cycle time of the production process. Because in the real world due to measurement errors and inaccuracies of information and the uncertainty of processes the concept Fuzzy is used. Therefore, in this study, the parameters for controlling the process of forming are optimized to reduce the cycle time. Since the optimization of an aspect of quality may modify other aspects, so, to overcome this problem, the multi-response method (Taguchi MRT) has been developed. Also, integrated fuzzy-neural network and data envelopment analysis is used for optimization and analysis purposes. Experimental results are done to measure the effectiveness of this approach in a manufacturing company in Iran.

Nursing Research on Patients with Hypertensive Intracerebral Hemorrhage Based on Fuzzy Neural Network Related Algorithm Under Postural Adjustment

2020 ◽  
Vol 10 (2) ◽  
pp. 422-427
Author(s):  
Dandan Qu ◽  
Caiyun Ding
Keyword(s):  
Neural Network ◽  
Supine Position ◽  
Fuzzy Neural Network ◽  
Blood Oxygenation ◽  
Postural Adjustment ◽  
Non Invasive ◽  
Significant Difference ◽  
Fuzzy Neural ◽  
The Difference ◽  
Multi Mode

Objective: To use the multi-mode fuzzy neural network monitoring measures explored in this project to clarify the trends of rSO2, ICP and CPP in patients with neurological HICH after different postures, and to screen the best nursing position for patients after HICH. Provide a basis for development. Methods: A total of 34 HICH patients admitted to the hospital from January 2017 to December 2018 were included in the study. The patient was placed in the supine position with the head position raised by 0°, 15°, 30°, 45°, 0°, and the interval between different body positions was 5 minutes. Each position was kept for 5 minutes, and the monitoring index values were read after stabilization. EGOS-600A near-infrared tissue blood oxygen parameters non-destructive monitor was used for bedside, dynamic and non-invasive real-time monitoring of rSO2; Codman intracranial pressure monitor was used for continuous dynamic monitoring of ICP; all patients were monitored with ICP and HPM-1205A ECG The monitor measures heart rate (HR), non-invasive blood pressure (BP), and pulse oximetry (SPO2). Results: When the bed angle of the patient was raised by 0°, 15°, 30° and 45°, the ICP value showed a decreasing trend with the elevation of the patient's bed angle. The ICP values were compared at various angles, and the difference was significant. Significance (P = 0.000); MAP value comparison, no significant difference (P = 0.074); CPP value showed an increasing trend with the elevation of the patient's bedside angle, the difference was significant, statistically significant (P = 0.000). There was no significant difference in HR values (P = 0.470). There was no significant difference in SPO2 values (P = 0.780). Conclusion: For patients with HICH with a GCS score of 5 to 12, multi-mode neurological monitoring, supine position elevation of 15°∼30° is a relatively suitable position; HICH patients with NIRS for rSO2 non-invasive monitoring is very Necessary, and should pay more attention to cerebral blood oxygenation changes in patients undergoing medical care after tracheotomy; use multiple modes of monitoring to ensure that patients SaO2, PaO2, MAP, ICP, CPP, and RSO2 are within the normal range. Choosing the most effective and reliable data support for the care and treatment measures that are most beneficial to improve the cerebral ischemia and hypoxia in patients has certain clinical value.

scholarly journals An Iterative Procedure for Optimizing the Performance of the Fuzzy-Neural Job Cycle Time Estimation Approach in a Wafer Fabrication Factory

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Toly Chen ◽  
Yi-Chi Wang
Keyword(s):  
Cycle Time ◽  
Empirical Relationship ◽  
Principal Component ◽  
Time Estimation ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Estimation Performance ◽  
Job Classification ◽  
Fuzzy Neural ◽  
Propagation Network

Estimating the cycle time of each job in a wafer fabrication factory is a critical task to every wafer manufacturer. In recent years, a number of hybrid approaches based on job classification (either preclassification or postclassification) for cycle time estimation have been proposed. However, the problem with these methods is that the input variables are not independent. In order to solve this problem, principal component analysis (PCA) is considered useful. In this study, a classifying fuzzy-neural approach, based on the combination of PCA, fuzzy c-means (FCM), and back propagation network (BPN), is proposed to estimate the cycle time of a job in a wafer fabrication factory. Since job classification is an important part of the proposed methodology, a new index is proposed to assess the validity of the classification of jobs. The empirical relationship between theSvalue and the estimation performance is also found. Finally, an iterative process is employed to deal with the outliers and to optimize the overall estimation performance. A real case is used to evaluate the effectiveness of the proposed methodology. Based on the experimental results, the estimation accuracy of the proposed methodology was significantly better than those of the existing approaches.

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.

Research of Genetic Algorithm Controller Based Fuzzy Neural Network in Vacuum Pre-Cooling Freshness-Keeping Machine

2014 ◽  
Vol 926-930 ◽  
pp. 3545-3549
Author(s):  
Ke Liang Zhou ◽  
Qiong Tan ◽  
Jian He
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Fuzzy Controller ◽  
Control Object ◽  
Bp Algorithm ◽  
Control Effect ◽  
Fuzzy Neural ◽  
The Difference

The control object is the temperature of pre-cooling machine, combined the advantage of neural network and genetic algorithm (GA). Adopting GA controller based fuzzy neural network. The controller doing the fuzzy reasoning to the difference of given temperature and sample temperature. GA does the offline training to the Connection weights and Membership function of fuzzy neural network, then uses BP algorithm to do further adjust online for parameters. Simulation result shows that the new controller achieves better control effect compared with traditional PID controller, fuzzy controller.

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