scholarly journals A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1430
Author(s):  
Yu-Cheng Wang ◽  
Horng-Ren Tsai ◽  
Toly Chen
Keyword(s):  
Cycle Time ◽  
State Of The Art ◽  
Random Search ◽  
Back Propagation ◽  
Time Range ◽  
Network Approach ◽  
Output Node ◽  
Learning Techniques ◽  
Optimal Values ◽  
Propagation Network

Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach is proposed to estimate the range of a cycle time, the results of which provide valuable information for many managerial purposes. The SFBPN approach is distinct from existing methods, because the thresholds on both the hidden and output layers of a back propagation network are fuzzified to tighten the range of a cycle time, while most of the existing methods only fuzzify the threshold on the output node. In addition, a random search and local optimization algorithm is also proposed to derive the optimal values of the fuzzy thresholds. The proposed methodology is applied to a real case from the literature. The experimental results show that the proposed methodology improved the forecasting precision by up to 65%.

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.

A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory

2012 ◽  
Vol 2 (2) ◽  
pp. 50-67 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Cycle Time ◽  
Back Propagation ◽  
Principal Component ◽  
Time Estimation ◽  
Cycle Times ◽  
Forecasting Performance ◽  
Semiconductor Manufacturer ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

Variable replacement is a well-known technique to improve the forecasting performance, but has not been applied to the job cycle time forecasting, which is a critical task to a semiconductor manufacturer. To this end, in this study, principal component analysis (PCA) is applied to enhance the forecasting performance of the fuzzy back propagation network (FBPN) approach. First, to replace the original variables, PCA is applied to form variables that are independent of each other, and become new inputs to the FBPN. Subsequently, a FBPN is constructed to estimate the cycle times of jobs. According to the results of a case study, the hybrid PCA-FBPN approach was more efficient, while achieving a satisfactory estimation performance.

scholarly journals AutoFolio: An Automatically Configured Algorithm Selector

2015 ◽  
Vol 53 ◽  
pp. 745-778 ◽  
Author(s):  
Marius Lindauer ◽  
Holger H. Hoos ◽  
Frank Hutter ◽  
Torsten Schaub
Keyword(s):  
State Of The Art ◽  
Machine Learning Techniques ◽  
Problem Instance ◽  
Algorithm Selection ◽  
The Core ◽  
Automated Algorithm ◽  
Learning Techniques ◽  
Algorithm Configuration ◽  
Optimal Values ◽  
The One

Algorithm selection (AS) techniques -- which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently -- have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4.

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.

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