Semi-dynamic Capacity Planning for Semiconductor Manufacturing
碩士 === 國立臺灣大學 === 工業工程學研究所 === 87 === Queueing and simulation methodologies have been applied to capacity modeling in semiconductor manufacturing. The advantage of queueing capacity model is the rapid computation time. The software that implements a queueing model can usually obtain estimates under...
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ndltd-TW-087NTU000300042016-02-01T04:12:23Z http://ndltd.ncl.edu.tw/handle/79561773586283862593 Semi-dynamic Capacity Planning for Semiconductor Manufacturing 半導體製造之半動態產能規劃模式 Chi-Rei Weng 翁啟瑞 碩士 國立臺灣大學 工業工程學研究所 87 Queueing and simulation methodologies have been applied to capacity modeling in semiconductor manufacturing. The advantage of queueing capacity model is the rapid computation time. The software that implements a queueing model can usually obtain estimates under two minutes for fabs that take hours to simulate. Simulation model could provide detail information but consume numerous computation load. However, product mix is changed rapid since the life cycle of the product is shorter than before and market demand is fluctuated with time. The manufacturing system would not be the steady state because of short product mix cycles and the long process flow production. The queueing capacity model based on steady state is invalid for the semiconductor manufacturing. This study developed semi-dynamic capacity model for non-steady state manufacturing system using queueing capacity model. This research contains queueing and semi-dynamic capacity models. First, the characteristic of semiconductor manufacturing is described. Then, a queueing capacity model and WIP tracking system were developed. The system evaluates non-steady state manufacturing system using agile simulation-based model. Yon-Chun Chou 周雍強 1999 學位論文 ; thesis 60 zh-TW |
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碩士 === 國立臺灣大學 === 工業工程學研究所 === 87 === Queueing and simulation methodologies have been applied to capacity modeling in semiconductor manufacturing. The advantage of queueing capacity model is the rapid computation time. The software that implements a queueing model can usually obtain estimates under two minutes for fabs that take hours to simulate. Simulation model could provide detail information but consume numerous computation load. However, product mix is changed rapid since the life cycle of the product is shorter than before and market demand is fluctuated with time. The manufacturing system would not be the steady state because of short product mix cycles and the long process flow production. The queueing capacity model based on steady state is invalid for the semiconductor manufacturing. This study developed semi-dynamic capacity model for non-steady state manufacturing system using queueing capacity model. This research contains queueing and semi-dynamic capacity models. First, the characteristic of semiconductor manufacturing is described. Then, a queueing capacity model and WIP tracking system were developed. The system evaluates non-steady state manufacturing system using agile simulation-based model.
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Yon-Chun Chou |
author_facet |
Yon-Chun Chou Chi-Rei Weng 翁啟瑞 |
author |
Chi-Rei Weng 翁啟瑞 |
spellingShingle |
Chi-Rei Weng 翁啟瑞 Semi-dynamic Capacity Planning for Semiconductor Manufacturing |
author_sort |
Chi-Rei Weng |
title |
Semi-dynamic Capacity Planning for Semiconductor Manufacturing |
title_short |
Semi-dynamic Capacity Planning for Semiconductor Manufacturing |
title_full |
Semi-dynamic Capacity Planning for Semiconductor Manufacturing |
title_fullStr |
Semi-dynamic Capacity Planning for Semiconductor Manufacturing |
title_full_unstemmed |
Semi-dynamic Capacity Planning for Semiconductor Manufacturing |
title_sort |
semi-dynamic capacity planning for semiconductor manufacturing |
publishDate |
1999 |
url |
http://ndltd.ncl.edu.tw/handle/79561773586283862593 |
work_keys_str_mv |
AT chireiweng semidynamiccapacityplanningforsemiconductormanufacturing AT wēngqǐruì semidynamiccapacityplanningforsemiconductormanufacturing AT chireiweng bàndǎotǐzhìzàozhībàndòngtàichǎnnéngguīhuàmóshì AT wēngqǐruì bàndǎotǐzhìzàozhībàndòngtàichǎnnéngguīhuàmóshì |
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1718173892844978176 |