在智慧工廠數位孿生技術的應用下，使用生產模擬系統工具已成為趨勢，將生產過程中的訂單量、產品、設備等實體生產物件，轉換成可控制的參數，從生產管理的角度延伸，如何快速反映生產狀況並執行產線優化，是許多企業共同發展的方向。本研究針對半導體封裝製程探討生產批量組合，以機台稼動率與產品流程時間為目標式，採用非支配排序基因演算法與田口方法，尋求最佳生產批量組合，為使生產模型更符合現況，導入多目標、多產品、流水線製程與批量生產等要素建立生產模擬模型，透過柏拉圖前緣解，在衝突目標間，提出適當的方案。另採多目標問題績效指標，比較兩者方法產生出的柏拉圖前緣解，發現非支配排序基因演算法搜索到的前緣解優於田口方法。本研究期望模擬最佳化結果提供給生產管理者快速且符合現況的資訊，輔以模擬可視化技術控管工廠，提升作業效率。

With the application of digital twin in smart factory, using a production simulation system has become the trend. For instance, orders, products and equipment can be converted into controllable parameter in the process of production. Moreover, in the extension of product management perspective, reflecting production condition immediately as well as executing production optimization are the main goals for many enterprises. This study focuses on discussing the configuration of processing batch size in IC-packaging industry. The objective function are utilization of machines and production flow time. The research method is non-dominated sorting genetic algorithm II and Taguchi method. To make a process model fits current situation, importing elements such as multi-objectives, multiple products, flow-line production and batch production in the simulation model, and proposing solutions through the Pareto - optimal front among goal conflict. The performance metrics of multi-objective problem is used to compare two Pareto-optimal solutions. Therefore, this research found Pareto-optimal front of non-dominated sorting genetic algorithm was superior to Taguchi method. This study expect to simulation-based optimization result to provide product manager immediate and suitable status information, supplemented by simulation visualization techniques to control the production and improve operational efficiency.

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