因全球高級豪華車近年來銷售成長逐漸上升，其間接提高零組件輪框加工廠的生產產量。本研究之案例公司為一家全球頂級鍛造鋁輪圈製造廠，目前生產排程方式依舊採傳統人為經驗法則並利用Excel工具進行人工作業派工，在資源有限下須考量整體場域限制及多目標績效指標，人為派工常導致生產排程績效不佳，此外輪框加工廠中主要分為四道製程，其中機加工站點第三小站(OP3)屬於瓶頸站點，若安排派工不當，常因一環節有所延誤導致後續生產環節物料遲延生產，使得後段機台產能容易造成閒置，造成極大成本損失，生產管理者欲了解在滿足訂單交期狀態下，須開幾台銑床機台較能滿足此訂單生產需求，期望透過開發一套修正多目標基因演算法求解於鋁輪圈加工廠瓶頸單站點問題，使得最小化機台數、總延誤時間、總換模時間。研究方法主要將既有文獻方法借以改善混合多目標基因演算法，並透過NSGAII以及TOPSIS尋求最佳解，其中為了能快速搜尋目標解空間以及跳出區域最佳解，將演算法的交配及突變率採用每20代動態修正法。在實證研究中，透過2種修正多目標基因演算法搭配不同加工排序編碼進行實驗，根據訂單到期日最近的優先處理編碼方式與傳統兩段隨機亂數生成指派機台及加工排序編碼比較，在此研究中能改善總延誤時間及換模時間至少25%左右，並提供適當開機機台數，且利用多準則決策方法論找出最適妥協解方案，能有效改善傳統人工作業效率並求得近似解方案。

Due to the gradual increase in the global sales of luxury cars in recent years, it indirectly increases the production of component wheel frame processing plants. The case company of this study is a world's top aluminum ring forging factory. At present, the production scheduling method still adopts the traditional human rule of thumb and dispatching by Excel tool. Under the limited resources, the overall field limitation and multi-objective performance indicators must be considered. In addition, the wheel and frame processing plant is mainly divided into four processes, and the third stage of the machine processing stage is a bottleneck stage. If the scheduling is inappropriate, the material production in the subsequent production link will be delayed due to the delay in one link, which makes the capacity of the machine in the latter section easy to be idle, resulting in great cost loss. The production manager wants to know how many machines must be started to meet the production demand of the order under the condition of meeting the delivery time, and expects to develop a modified multi-objective genetic algorithm to solve the bottleneck single stage problem in aluminum ring processing plant, so as to minimize the number of machines, total delay time and total setup time. The research methods mainly used existing literature methods to improve the current multi- objective genetic algorithm, and sought the best solution through NSGAII and TOPSIS. In order to quickly search the target solution space and jump out of the regional best solution, the crossover and mutation rate of the algorithm were dynamically modified every 20 generations. In empirical study, through two kinds of modified multi-objective genetic algorithm with different processing sort coding experiments, according to the order EDD sort coding method and the traditional two random random number assigned to the machine and the processing sequence generated code comparison, in this study can improve the total delay time and the time of the mould at least 25% and provide proper boot up the machine number, The multi-criteria decision-making methodology(MCDM) is used to find the optimal compromise solution, which can effectively improve the efficiency of traditional manual operation and obtain the approximate solution.

經濟部統計處（2022），Available: https://www.moea.gov.tw/MNS/dos/home/Home.aspx
李沛倚（2021）。應用多目標基因演算法於平準化生產－以鍛造輪圈加工廠為例。國立成功大學製造資訊與系統研究所碩士論文。
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