An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing
Rapid automated logistics within a factory are essential to maximize productivity. In semiconductor manufacturing, the most important logistics management is the efficient operation of overhead hoist transports (OHTs). To transfer wafers via OHTs without delays, it is necessary to predict short-term...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11071687/ |
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| author | Donghun Lee Suhee Kim Hoonseok Park Haejoong Kim Ri Choe Younkook Kang Jae-Yoon Jung Kwanho Kim |
| author_facet | Donghun Lee Suhee Kim Hoonseok Park Haejoong Kim Ri Choe Younkook Kang Jae-Yoon Jung Kwanho Kim |
| author_sort | Donghun Lee |
| collection | DOAJ |
| description | Rapid automated logistics within a factory are essential to maximize productivity. In semiconductor manufacturing, the most important logistics management is the efficient operation of overhead hoist transports (OHTs). To transfer wafers via OHTs without delays, it is necessary to predict short-term traffic congestion in the OHT railway accurately. However, the congestion prediction is a significant challenge due to the complexity of investigating all traffic conditions and dynamic traffic changes. Several studies have utilized machine learning approaches to address these concerns, but limitations arise in predicting the short-term congestion due to the performance bias stemming from large input features. Recurrent neural networks are effective in predicting traffic flow in transportation. However, they may not be suitable for OHT railway congestion prediction due to unpredictable loading/unloading events and varying traffic volumes. Therefore, this study proposes an integrated neural network-based method where multiple neural networks are trained considering current conditions of the railway network and expected changes in traffic conditions. To verify the effectiveness of the proposed method, a simulated dataset was used to reflecting real-world semiconductor fabrication. The experiment results demonstrate that the proposed method outperforms existing methods, including machine learning- and deep learning-based methods. |
| format | Article |
| id | doaj-art-c28898867d494e17a50e11bf6fea02e1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c28898867d494e17a50e11bf6fea02e12025-08-20T02:41:42ZengIEEEIEEE Access2169-35362025-01-011312163012164010.1109/ACCESS.2025.358591411071687An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor ManufacturingDonghun Lee0https://orcid.org/0000-0003-2141-6761Suhee Kim1https://orcid.org/0009-0003-5897-1950Hoonseok Park2Haejoong Kim3Ri Choe4Younkook Kang5Jae-Yoon Jung6https://orcid.org/0000-0002-4850-6284Kwanho Kim7https://orcid.org/0000-0002-9487-2365Department of Industrial and Management Engineering, Incheon National University, Incheon, Republic of KoreaDepartment of Industrial and Management Engineering, Incheon National University, Incheon, Republic of KoreaDepartment of Big Data Analytics, Kyung Hee University, Yongin, Republic of KoreaDepartment of Industrial and Management Engineering, Kyonggi University, Suwon, Republic of KoreaMaterial Handling Automation Group, Samsung Electronics, Hwaseong, Republic of KoreaMaterial Handling Automation Group, Samsung Electronics, Hwaseong, Republic of KoreaDepartment of Big Data Analytics, Kyung Hee University, Yongin, Republic of KoreaDepartment of Industrial and Systems Engineering, Dongguk University, Seoul, Republic of KoreaRapid automated logistics within a factory are essential to maximize productivity. In semiconductor manufacturing, the most important logistics management is the efficient operation of overhead hoist transports (OHTs). To transfer wafers via OHTs without delays, it is necessary to predict short-term traffic congestion in the OHT railway accurately. However, the congestion prediction is a significant challenge due to the complexity of investigating all traffic conditions and dynamic traffic changes. Several studies have utilized machine learning approaches to address these concerns, but limitations arise in predicting the short-term congestion due to the performance bias stemming from large input features. Recurrent neural networks are effective in predicting traffic flow in transportation. However, they may not be suitable for OHT railway congestion prediction due to unpredictable loading/unloading events and varying traffic volumes. Therefore, this study proposes an integrated neural network-based method where multiple neural networks are trained considering current conditions of the railway network and expected changes in traffic conditions. To verify the effectiveness of the proposed method, a simulated dataset was used to reflecting real-world semiconductor fabrication. The experiment results demonstrate that the proposed method outperforms existing methods, including machine learning- and deep learning-based methods.https://ieeexplore.ieee.org/document/11071687/Integrated neural networksshort-term traffic congestion predictionsemiconductor fabricationsustainable operation |
| spellingShingle | Donghun Lee Suhee Kim Hoonseok Park Haejoong Kim Ri Choe Younkook Kang Jae-Yoon Jung Kwanho Kim An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing IEEE Access Integrated neural networks short-term traffic congestion prediction semiconductor fabrication sustainable operation |
| title | An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing |
| title_full | An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing |
| title_fullStr | An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing |
| title_full_unstemmed | An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing |
| title_short | An Integrated Neural Network-Based Traffic Congestion Prediction for Material Handling Systems of Semiconductor Manufacturing |
| title_sort | integrated neural network based traffic congestion prediction for material handling systems of semiconductor manufacturing |
| topic | Integrated neural networks short-term traffic congestion prediction semiconductor fabrication sustainable operation |
| url | https://ieeexplore.ieee.org/document/11071687/ |
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