TFT-LCD之製造流程依序為Array、Cell（LCD）與Module（LCM）製程。面板供料規劃牽涉到多廠環境、產品多樣化、面板等級、替代料以及顧客對品質與用料的要求等。由於LCM廠面板的供料請求日益複雜，LCD廠規劃人員很難同時考量所有的複雜條件。本研究之個案公司目前採用單廠輪流進行人工需求分配規劃（切量規劃）之方式，容易產生失誤之決策和造成前段廠區庫存不穩定，因此LCD廠與LCM廠規劃人員需花費額外時間更新計劃，以致拖長規劃時間。有鑑於此，本研究在分析規劃環境特性以及個案公司現行規劃方式之缺點後，建構一同時考量多廠之整體切量規劃模組，以解決現行人工規劃之困難，並以實際案例，分析比較本研究所提出之模型與人工規劃方式之結果。
本研究建構之模組，首先進行產品分類規劃，從中考量各產品共用之物料、物料稀少性以及供給廠數，將所有產品分成三個求解階段，並考量各廠期初庫存、各項生產參數、面板輸入等級限制、產品路徑以及用料限制等，以最小化懲罰成本為目標，建構一混整數規劃模型，並進行求解，以得到最佳多廠需求分配。
案例測試結果顯示，本研究所發展之整體切量規劃模組，不僅能有效地降低呆料以及穩定庫存數量，滿足個案公司之預期需求，並可以大幅減少現行人工作業所需之規劃時間。

The manufacturing of TFT-LCD includes array manufacturing process, cell (LCD) manufacturing process, and module assembly (LCM) process. The material planning of panel production involves multi-site production environment, product variety, panel grades, substitute components, customer specified quality and raw material, and so on. Because panel requests of LCM factories are increasingly complicated, it is difficult for LCD factory’s planners to consider all the complex conditions simultaneously when planning for the requests. The current practice at the case company is sequentially planning demand allocation (Cross Fab Planning), one factory at a time. It is easy to make inappropriate decisions or result in volatile inventory levels using this approach. Thus, planners at LCD factories and LCM factories have to spend extra time and efforts to amend the schedule, which prolongs the planning time. To resolve this issue, we first surveyed the characteristics of the planning environment and the drawbacks of the planning method currently used at the case company. Based on these, this research developed an integrated cross fab planning module which considers all factories simultaneously. The results resulted from the proposed module are compared with the manual results of current practice.
The integrated cross fab planning module first classifies products into groups by their common components, material scareness, and the number of manufacturing plants. The resulting product groups are then solved in three stages considering each factory’s initial inventory, production parameters, panel input grade constraints, product routings, material requirement and other factors. We proposed a mix-integer programming model to minimize the penalty cost for this problem.
Experimental results show that the proposed integrated cross fab planning module is capable of effectively reducing dead inventory, stabilizing inventory levels, meeting the case company’s demand forecasts, and shortening much of the planning time required by the current manual process used by the case company.

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