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.

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.

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 Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression

2020 ◽  
Author(s):  
Hui Tian ◽  
Andrew Yim ◽  
David P. Newton
Keyword(s):  
Quantile Regression ◽  
Forecast Error ◽  
Simulated Data ◽  
Ordinary Least Squares ◽  
Shape Effect ◽  
Forecasting Accuracy ◽  
Forecasting Performance ◽  
Cash Flow Forecasting ◽  
Positive Effect ◽  
Better Than

We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry. This paper was accepted by Shiva Rajgopal, accounting.

Pooling in Tourism Demand Forecasting

2018 ◽  
Vol 58 (7) ◽  
pp. 1161-1174 ◽  
Author(s):  
Wen Long ◽  
Chang Liu ◽  
Haiyan Song
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Demand Forecasting ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Spatial Effects ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Demand Models ◽  
Forecasting Performance

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

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 Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1931 ◽  
Author(s):  
Yechi Zhang ◽  
Jianzhou Wang ◽  
Haiyan Lu
Keyword(s):  
Power Distribution ◽  
Data Preprocessing ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Power Stations ◽  
South Wales ◽  
Model Combining ◽  
Forecasting Performance ◽  
Preprocessing Technique

Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

scholarly journals Forecasting Volatility: Evidence from the Saudi Stock Market

2018 ◽  
Vol 11 (4) ◽  
pp. 84 ◽  
Author(s):  
Naseem Al Rahahleh ◽  
Robert Kao
Keyword(s):  
Stock Market ◽  
Policy Makers ◽  
Forecasting Accuracy ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Asymmetric Power ◽  
International Investors ◽  
Forecasting Volatility ◽  
Class Models ◽  
Petrochemical Industries

The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations.

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.

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