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)。

目錄
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍 4
1.4 研究步驟與方法 4
第二章 文獻回顧 7
2.1 排程問題 7
2.2 解決排程問題的相關方法論 9
2.3 FMS同步排程(SIMULTANEOUS SCHEDULING) 11
2.4 替代機台(ALTERNATIVE MACHINE) 12
2.5 基因演算法 12
2.6 模擬最佳化方法 14
2.7 多目標最佳化問題 17
2.8 多目標基因演算法 19
第三章 彈性製造系統之機台與車輛同步排程問題 22
3.1 FMS同步排程問題 22
3.2 問題描述 23
3.3 問題定義與假設 23
3.4 方法論 25
3.4.1 基因演算法 26
3.4.2 模擬模式架構 34
3.4.3 模擬模式建構 35
第四章 單目標之確定型模擬模式與實驗 42
4.1 實驗一：使用基因演算法結合LOCAL SEARCH求解同步排程問題 42
4.1.1 基因演算法結合Local Search 43
4.1.2 基因演算法之參數分析 47
4.1.3 實驗一之模擬結果 50
4.2 實驗二：導入區域控制以避免衝撞與鎖死 54
4.2.1 避免衝撞與鎖死之方法論 55
4.2.2 實驗二之模擬結果 58
4.3 實驗三：在FMS考慮替代機台應用於同步排程 61
4.3.1 處理替代機台問題之方法論 64
4.3.2 實驗三之模擬結果 68
4.4 實驗四：考慮替代機台下再加入機台利用率平衡之限制 72
4.4.1 考慮替代機台下再加入利用率平衡之方法論 72
4.4.2 實驗四之實驗結果 73
4.5 實驗結論 79
第五章 單目標之隨機型模擬模式與實驗 81
5.1 使模型具有隨機性 81
5.1.1 考慮加工時間具有隨機性 81
5.1.1 考慮隨機性之實驗結果 83
5.2 基因演算法結合OCBA之方法論 88
5.2.1 基因演算法與OCBA之流程 90
5.2.2 OCBA之參數分析 93
5.2.3 OCBA與Equal Allocation模擬效率比較 98
5.3 實驗結論 99
第六章 雙目標之模擬模式與實驗 102
6.1 使用多目標基因演算法處理同步排程問題 102
6.1.1 Pareto Optimality 102
6.1.2 多目標基因演算法模式 (Multi-Objective Genetic Algorithm, MOGA) 104
6.1.3 多目標基因演算法之實驗結果 108
6.2 考慮加工時間具有隨機性 110
6.2.1 考慮隨機性之實驗結果 111
6.3 多目標基因演算法結合MOCBA方法論 114
6.3.1 多目標基因演算法與MOCBA之流程 116
6.3.2 MOCBA與Equal Allocation模擬效率比較 120
6.4 實驗結論 123
第七章 結論與建議 125
7.1 結論 125
7.2 建議 127
參考文獻 128

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