scholarly journals Precise and Accurate Job Cycle Time Forecasting in a Wafer Fabrication Factory with a Fuzzy Data Mining Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Toly Chen ◽  
Richard Romanowski
Keyword(s):  
Data Mining ◽  
Cycle Time ◽  
Principal Component ◽  
Wafer Fabrication ◽  
Fuzzy Data ◽  
Lower And Upper Bounds ◽  
Forecasting Accuracy ◽  
Data Mining Approach ◽  
Backpropagation Network ◽  
Better Than

Many data mining methods have been proposed to improve the precision and accuracy of job cycle time forecasts for wafer fabrication factories. This study presents a fuzzy data mining approach based on an innovative fuzzy backpropagation network (FBPN) that determines the lower and upper bounds of the job cycle time. Forecasting accuracy is also significantly improved by a combination of principal component analysis (PCA), fuzzy c-means (FCM), and FBPN. An applied case that uses data collected from a wafer fabrication factory illustrates this fuzzy data mining approach. For this applied case, the proposed methodology performs better than six existing data mining approaches.

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 Evaluation of Distance Between Pedestrian Crossings by Students in One of the Polish Cities

2013 ◽  
Vol 59 (4) ◽  
pp. 547-559
Author(s):  
M. Kruszyna
Keyword(s):  
Data Mining ◽  
City Centre ◽  
Fuzzy Data ◽  
Two Dimensional ◽  
Data Mining Approach ◽  
Membership Value ◽  
Third Dimension ◽  
The City

Abstract In this paper, the distances between pedestrian crossings in twenty one places in the city of Wrocław, together with their evaluation by the researched groups of students, were analyzed. The database created from the collected questionnaires contains a set of two-dimensional variables: the distance between crossings and the rating of the students. The database set was analyzed using a fuzzy data mining approach to create particular clusters. Various numbers of clusters were analyzed, and the division of data into three clusters made it possible to relate the analysis to the LOS methodology. Each variable was enriched with a third dimension representing a membership value. The obtained evaluated distances are similar to values recommended in literature, although the distances highly evaluated by the students do not often occur in reality. This might suggest that there is the need to create new crossings, especially in the city centre, where pedestrian trafic is or should be important.

Data Mining Well Logs for Optimum Well Placement

2020 ◽  
Author(s):  
Anuroop Pandey ◽  
Mohammed F. Al Dushaishi ◽  
Espen Hoel ◽  
Svein Hellvik ◽  
Runar Nygaard
Keyword(s):  
Data Mining ◽  
Gamma Ray ◽  
Principal Component ◽  
Decision Criterion ◽  
Classification Model ◽  
Well Logs ◽  
Well Placement ◽  
Reservoir Rocks ◽  
Data Mining Approach ◽  
Gamma Ray Log

Abstract Well placement with geosteering can get very complex in reservoirs with formation change not simply addressed by changes in the gamma ray log response. This paper uses data mining to characterize complex reservoirs for optimum well placement. The objective of this paper is to develop a data mining process to evaluate non-trivial geologic settings for geosteering reservoir well placement. The well logs’ data was collected from multiple wells in a Norwegian North Sea field, where the reservoir rocks are characterized with high heterogeneities. Principal component analysis was used to recognize data pattern and extract underlying features. The extracted features are then into distinct groups using Hierarchical clustering (HC) analysis. A classification model, that is based on the deviance analysis, was constructed to build a criterion to identify each cluster within a set of well log data. The results show that the data mining approach sufficiently identified highly heterogeneous formations and can be used for geosteering applications. Classification trees defined quantitative decision criterion for the identified clusters. The approach is capable of distinguishing between potential and non-potential steering clusters, as the identified clusters have distinct decision criteria and effectively explain the variations within a section, as verified with the lithology described from core analysis.

scholarly journals Fuzzy dynamic-prioritization agent-based system for forecasting job cycle time in a wafer fabrication plant

2021 ◽  
Author(s):  
Tin-Chih Toly Chen ◽  
Yu-Cheng Wang
Keyword(s):  
Cycle Time ◽  
Wafer Fabrication ◽  
Forecasting Accuracy ◽  
Agent Based ◽  
Wafer Fab ◽  
Forecasting Performance

AbstractA fuzzy dynamic-prioritization agent-based system was developed in this study to improve the forecasting of the cycle time of a job in a wafer fabrication plant (wafer fab). In this system, multiple fuzzy agents forecast the cycle time of a job from various viewpoints, after which the aggregation and evaluation agent aggregates these fuzzy cycle time forecasts using an innovative operator (i.e., the fuzzy weighted intersection) into a single representative value. Subsequently, the optimization agent varies the authority levels of the fuzzy cycle time forecasting agents to optimize the forecasting performance. A practical example was used to evaluate the effectiveness of the fuzzy dynamic-prioritization agent-based system. The experiment results indicated that the fuzzy dynamic-prioritization agent-based system outperformed three rival methods in improving forecasting accuracy. In addition, the forecasting performance could be enhanced by discriminating the authority levels of the fuzzy cycle time forecasting agents.

Factory Cycle-Time Prediction With a Data-Mining Approach

2006 ◽  
Vol 19 (2) ◽  
pp. 252-258 ◽  
Author(s):  
P. Backus ◽  
M. Janakiram ◽  
S. Mowzoon ◽  
G.C. Runger ◽  
A. Bhargava
Keyword(s):  
Data Mining ◽  
Cycle Time ◽  
Time Prediction ◽  
Data Mining Approach
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