The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication
An innovative classification and back-propagation-network tree (CABPN tree) approach is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory, which is one of the most important tasks in controlling the wafer fabrication factory. The CABPN tree approach is an exte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-05-01
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| Series: | Symmetry |
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| Online Access: | http://www.mdpi.com/2073-8994/6/2/409 |
| _version_ | 1798035003800551424 |
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| author | Toly Chen |
| author_facet | Toly Chen |
| author_sort | Toly Chen |
| collection | DOAJ |
| description | An innovative classification and back-propagation-network tree (CABPN tree) approach is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory, which is one of the most important tasks in controlling the wafer fabrication factory. The CABPN tree approach is an extension from the traditional classification and regression tree (CART) approach. In CART, the cycle times of jobs of the same branch are estimated with the same value, which is far from accurate. To tackle this problem, the CABPN tree approach replaces the constant estimate with variant estimates. To this end, the cycle times of jobs of the same branch are estimated with a BPN, and may be different. In this way, the estimation accuracy can be improved. In addition, to determine the optimal location of the splitting point on a node, the symmetric partition with incremental re-learning (SP-IR) algorithm is proposed and illustrated with an example. The applicability of the CABPN tree approach is shown with a real case. The experimental results supported its effectiveness over several existing methods. |
| first_indexed | 2024-04-11T20:52:05Z |
| format | Article |
| id | doaj.art-901ccaf1a96842f1ab7e779c21d645c9 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T20:52:05Z |
| publishDate | 2014-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-901ccaf1a96842f1ab7e779c21d645c92022-12-22T04:03:49ZengMDPI AGSymmetry2073-89942014-05-016240942610.3390/sym6020409sym6020409The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer FabricationToly Chen0Department of Industrial Engineering and Systems Management, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung City 407, TaiwanAn innovative classification and back-propagation-network tree (CABPN tree) approach is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory, which is one of the most important tasks in controlling the wafer fabrication factory. The CABPN tree approach is an extension from the traditional classification and regression tree (CART) approach. In CART, the cycle times of jobs of the same branch are estimated with the same value, which is far from accurate. To tackle this problem, the CABPN tree approach replaces the constant estimate with variant estimates. To this end, the cycle times of jobs of the same branch are estimated with a BPN, and may be different. In this way, the estimation accuracy can be improved. In addition, to determine the optimal location of the splitting point on a node, the symmetric partition with incremental re-learning (SP-IR) algorithm is proposed and illustrated with an example. The applicability of the CABPN tree approach is shown with a real case. The experimental results supported its effectiveness over several existing methods.http://www.mdpi.com/2073-8994/6/2/409cycle timeestimationclassification and regression treesymmetric partitioningback propagation networkwafer fabrication |
| spellingShingle | Toly Chen The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication Symmetry cycle time estimation classification and regression tree symmetric partitioning back propagation network wafer fabrication |
| title | The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication |
| title_full | The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication |
| title_fullStr | The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication |
| title_full_unstemmed | The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication |
| title_short | The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication |
| title_sort | symmetric partitioning and incremental relearning classification and back propagation network tree approach for cycle time estimation in wafer fabrication |
| topic | cycle time estimation classification and regression tree symmetric partitioning back propagation network wafer fabrication |
| url | http://www.mdpi.com/2073-8994/6/2/409 |
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