本研究針對特殊混合流程型生產系統之短期生產計劃中的生產排程進行訂單的指派規劃，其規劃為將訂單指派到各站適合的機台群。本研究主要探究三生產站，每站包含多種機台群，每機台群包含一至多台的完全相同平行機台，並且考量機台在加工不同種類產品時會有一整備時間(Setup time)。各種產品在各站並非所有機台群皆可進行加工，因此，在規劃時就必須全盤考量各產品對於各站機台群選擇的限制。綜觀以上特性，當訂單量愈大，將會使得指派規劃相當複雜和困難；若是指派不適當，將導致生產系統之產能無法有效利用。
本研究之主要瓶頸站為B站，該站之生產最耗時，且其擁有的機台數量亦最多，為整條產線的資本支出之重點。本研究採用Pareto Optimality的概念將B站的機台平均利用率與訂單的平均流程時間做為績效指標進行同時考量。本研究為一考慮多生產站、多機台型、多產品的訂單指派規劃，研究方法為利用模擬軟體在考量所有機台、產品、訂單等資訊後，建構一模擬模式，使該模式符合本研究之生產系統。接著，在面臨可行解空間過大時，進一步設計多目標基因演算法(Multi-Objective Genetic Algorithm, MOGA)進行可行解域中最佳方案的搜尋；而因應機台的加工時間具有隨機性之情況，則需經由多次的抽樣來縮減變異，此將造成模擬時間的增加，因此，本研究將利用多目標最佳運算資源分配法(Multi-objective Optimal Computing Budget Allocation, MOCBA)來有效地提升模擬效率。最後，本研究針對所使用之模擬最佳化方法進行參數分析，希望藉此提升模擬之效率與Pareto set之品質；另外，本研究亦比較兩種多目標基因演算法(NSGA-II & SPEA)分別結合MOCBA在求解此問題時的Diversity metric與模擬效率，研究結果發現，當使用NSGA-II結合MOCBA可得較佳之結果。

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 3
1.4 研究步驟與方法 4
第二章 文獻回顧 5
2.1 混合流程型生產排程問題 5
2.2 多目標模擬最佳化 9
2.2.1 模擬最佳化方法 9
2.2.2 多目標最佳化問題 10
2.2.3 多目標基因演算法 11
2.2.4 多目標最佳運算資源分配 13
第三章 問題定義與分析 16
3.1 混合流程型生產排程問題 16
3.2 問題描述 17
3.2.1 訂單資訊 17
3.2.2 產品資訊 18
3.2.3 機台配置資訊 19
3.3 問題定義與分析 20
第四章 多目標基因演算法 25
4.1 模擬模式的建構 25
4.1.1 模擬模式建構的目的 25
4.1.2 模擬模式之範圍與細緻度 25
4.1.3 模擬工具 26
4.1.4 混合流程型生產系統模擬模式的建立 26
4.2 多目標基因演算法處理混合流程型生產排程問題 30
4.2.1 NSGA-II建構模式 30
4.2.2 SPEA建構模式 36
4.3 多目標基因演算法結合MOCBA之方法論 38
4.3.1 MOCBA方法論 39
4.3.2 多目標基因演算法結合MOCBA之流程 41
第五章 實驗與參數分析 45
5.1 考慮隨機性 45
5.1.1 考慮加工時間具有隨機性 45
5.2 MOCBA與傳統方法之比較 49
5.2.1 實驗結果與分析 50
5.3 NSGA-II與SPEA之比較 52
5.3.1 實驗結果與分析 53
5.4 NSGA-II與MOCBA之參數分析 55
5.4.1 NSGA-II實驗結果與分析 55
5.4.2 MOCBA實驗結果與分析 58
5.5 訂單量情境分析 61
5.5.1 實驗結果與分析 62
5.6 不同決策考量分析 64
5.6.1 實驗結果與分析 64
第六章 結論與建議 67
6.1 結論 67
6.2 建議 69
參考文獻 70
附錄 74

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