摘要
傳統上之排程問題只考慮機台資源，但是在彈性製造系統(Flexible Manufacturing System, FMS)中除了機台資源外還包含搬運系統如自動物料搬運車輛(Automatic guided vehicles, AGV)，而搬運工件的時間會導致機台的閒置，因此在本研究的目標是要同步(Simultaneous)處理作業排程與車輛排程，使總完工時間(Makespan)最小化。同時考量作業排程與車輛排程是一個複雜的NP-Hard問題，因此需要一個有效的搜尋方法。根據文獻指出演化式演算法在處理最佳化排程問題是較好的選擇，因此本研究利用基因演算法(Genetic Algorithm ,GA)產生作業排程，並利用車輛排程演算法對每一個作業去選擇車輛。將兩個方法結合以處理同時排程問題。在面臨多目標問題時則使用多目標基因演算法(Multi-Objective Genetic Algorithm, MOGA)。
在FMS當中，由於機台都為多功能機台，工件的同一作業可以選擇替代機台進行加工，這個特性更增加排程的複雜性；而替代機台會影響到總完工時間以及機台與車輛的利用率，因此除了利用機台選擇演算法使總完工時間最小化之外，機台的利用率平衡也是重要的指標。在文獻中以數學規劃求解同時排程問題，車輛搬運工件的時間都是以搬運時間除以車輛移動速度，並未考慮到車輛在途中可能因為壅塞而延遲搬運時間或是車輛鎖死(deadlock)的實際上會面臨到問題，因此本研究在FMS同步排程系統之建構以離散事件模擬來達到考量實務情況，以區域控制(zone-control)處理車輛鎖死的問題，並以模擬最佳化架構在基因演算法結合模擬模式來增快求解效率。
FMS當中的機台都為CNC機台，作業的加工時間幾乎都是確定的，但是作業的整備時間是具有變異性的，這個因素使整個模型具有隨機性，若只以模擬一次的數據便對其下定論，可能因為隨機造成的誤差導致決策錯誤，若模擬太多次則會導致模擬時間過長。因此利用OCBA(Optimal Computing Budget Allocation)分配較多模擬資源給無法明確分辨好壞與變異太大的方案，在滿足特定的P{CS}下，能利用最少的模擬資源找出準確的方案。在面臨多目標問題時則使用MOCBA((Multi-Objective Optimal Computing Budget)。

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