DC FieldValueLanguage
dc.contributor.author張文珍en_US
dc.contributor.authorWen Jen Changen_US
dc.contributor.author巫木誠en_US
dc.contributor.authorMuh-Cherng Wuen_US
dc.date.accessioned2014-12-12T02:12:03Z-
dc.date.available2014-12-12T02:12:03Z-
dc.date.issued2007en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT009133818en_US
dc.identifier.urihttp://hdl.handle.net/11536/57968-
dc.description.abstract中文摘要 產能規劃是半導體產業的一個重要議題，依據規劃的時間幅度，可區分為：長期、中期及短期等三類。本論文係探討短期產能規劃問題，主要探討兩晶圓廠在短期(一週)內，當各廠產能供需失衡時，兩廠該如何進行產能交易決策，方能最大化兩廠的長期總和利潤。 本論文分成兩個研究主題進行。第一個主題乃根據單項決策準則(single decision-making criterion)，發展兩晶圓廠進行短期(每週)產能交易的決策機制，期能最大化兩廠的長期總產量。此單項決策準則是指兩晶圓廠每週的總產出作業數。第二個主題為前一主題的擴充性研究，亦即將單項決策準則擴充為多項決策準則(multiple decision-making criteria)，以兩晶圓廠的三種總產出指標當成決策準則—每週總產出作業數、每週總加工層數、每週總產出晶圓數。為將三個決策準則整合成單一準則，本主題之研究重點為：找出三個決策準則的最佳權重組合，期能最大化兩晶圓廠的長期利潤總和。 研究結果顯示：本論文所發展的產能交易方法，不論是採單一或多項決策準則，均能有效提高晶圓廠長期的總和利潤。其中多項決策準則的績效又較單項決策準則為佳。zh_TW
dc.description.abstractThis paper developed two methods for trading weekly tool capacity between two semiconductor fabs. Due to the high-cost characteristics of tools, a semiconductor company with multiple fabs (factories) may weekly trade their tool capacities. That is, a lowly-utilized workstation in one fab may sell capacity to its highly-utilized counterpart in the other fab. The first method is a trading decision-making mechanism based on a single criterion—number of weekly produced operations. The second method, an extension of the first one, is a multiple criteria trading decision approach. Three decision criteria are used: number of operations, number of layers, and number of wafers. Additionally, a way to find an optimal weighting vector for integrating the three criteria is developed. Experiments indicated that the two capacity trading methods we proposed can both effectively increase the aggregate long-term profit of the two fabs. In addition to the multiple criteria approach indeed outperformed the single-criterion method.en_US
dc.language.isozh_TWen_US
dc.subject晶圓廠zh_TW
dc.subject產能交易zh_TW
dc.subject類神經網路zh_TW
dc.subject基因演算法zh_TW
dc.subject實驗設計zh_TW
dc.subjectSemiconductoren_US