薄膜電晶體液晶顯示器(Thin Film Transistor Liquid Crystal Display, TFT-LCD)因其輕薄短小及具省電之特性，近年來已逐漸取代傳統陰極射線管顯示器(Cathode Ray Tube, CRT)。而由於其產品生命週期越來越短，顧客所下訂單之交期也相對變短，連帶使得市場的供需環境逐漸波動受到衝擊。面板產業產品種類多、變異性大，因此完工時間必須有效縮短，且及時滿足顧客需求，以掌握市場先機。

本研究討論TFT-LCD平行機台派工問題，一般面板廠是採取人工方式管制生產排程，但客戶端的訂單與供應商的物料交期變動頻繁，而人工調整計畫非常費時，導致訂單調度缺乏彈性，因此不得不重新發展更合適的生產規劃與排程系統，以解決此生產問題。由於非等效平行機台排程問題屬於NP-hard問題，複雜度高且不易求解，當問題規模變大時，使用傳統最佳化方法求解顯得較無效率，而基因演算法可多點同步搜尋，避免陷入區域最佳解，因此本研究以基因演算法為基礎求解多種績效衡量指標下之非等效平行機台排程問題，並以LINGO軟體求解本研究數學模式，並將兩者所得到的解與求解時間加以比較，以驗證本研究演算法之效率。在實證測試方面，將實際作業融入演算法中，由實驗得知以基因演算法為基礎之啟發式演算法有著不錯的求解品質及求解效率。

Thin Film Transistor Liquid Crystal Display (TFT-LCD) has gradually replaced the traditional cathode ray tube (CRT) display in recent years because of its compact size and power-down feature, which leads to several challenges that manufactures have to face, such as a small amount of production volume, product diversification, shorter product life cycles, and decreasing order delivery times.

This study deals with the dispatching problem for parallel machines in TFT-LCD assembly processes. Since orders from clients and material from suppliers would constantly change with time, which results in the requirement of flexibility for scheduling of orders, the TFT-LCD manufacturer has to develop a more appropriate production planning and scheduling system to solve the production problem. Because the non-equivalent parallel machine scheduling problem is NP-hard, and it has high complexity and is not easy to be solved and moreover, when the problem scales bigger, it becomes un-efficient to use traditional optimization methods, this study is thus based on the genetic algorithm to solve the unrelated parallel machine scheduling problem through the setting of a variety of performance measurement indicators. In the end, we compare the solutions from the Genetic algorithm with these from LINGO and analyze them, and the results show that the solutions from the Genetic algorithm are not worse than these from LINGO but with shorter computation time. Therefore, it saves lots of time and has an acceptable quality to have a solution through the Genetic algorithm.

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