本研究針對混合流程型生產系統之生產排程，探討訂單拆批與機台指派同步規劃。拆批為將訂單拆分成數個子批，機台指派則為將訂單指派到各站可加工的機台群。本研究探究三生產站之混合流程型生產系統，每站有多個非等效平行機台群，每機台群又包含一至數台的等效平行機台。考量到訂單加工時間具有隨機性，各產品依照期產品規格，對於各站機台群又有選擇的限制。為了降低訂單平均完成時間，批量分割將會在此生產系統使用，將各訂單拆成數個子批，藉由加工作業重疊，以加快訂單在這三站的流程時間。
綜觀上述各點，本研究將探討二大問題，一為批量分割，二為機群指派。批量分割是將各訂單分割成數個相同大小的子批，並找尋合適的分割數量；機群指派則是將訂單指派至各站適合之加工機群。在同步考量拆批與機台指派的情況下，使得求解空間過大、問題過於複雜，因此本研究使用基因演算法進行可行解域中的方案搜尋。從結果可以發現，拆批確實可以降低訂單平均完成時間，而且變動拆批績效表現較等量拆批佳。除此之外，考量到訂單加工時間具有隨機性之情況，需藉由多次的抽樣來減少變異，因而增加模擬次數。因此，本研究利用資源分配最佳化(Optimal Computing Budget Allocation, OCBA)來提升模擬效率，結果顯示可以降低76%的模擬資源。

In this research we study a hybrid flow shop scheduling with lot split and machine assignment problem, the order would be split up several sublots, and the machine assignment is that each order would be assigned to eligibility machine groups at each stage. In this research we study a three-stage hybrid flow shop production system, with several unrelated parallel machine groups at each stage, and there are some identical parallel machines at each machine group. We consider that processing time is stochastic, and each product type has its eligible machine groups. To reduce mean completion time, lot split would be used in this production system. Each order would be separated into several sublots to reduce flow time by the overlapping of operations.
All in all, this research would discuss two issues, lot split and machine group assignment. Lot split is that each order would be separated into several equal size sublots, and we look for suitable quantity. Machine group assignment is that each order would be assigned to eligible machine groups at each stage. We simultaneously consider lot split and machine group assignment, so that solution space is too large. We use genetic algorithm to search for suitable solution. From the result, we find that lot split is useful to mean completion time, and variable lot split performs better than consistent lot split. In addition, we take stochastic processing time into condition, and it spends many times to reduce the sampling variability. Therefore, we use optimal computing budget allocation to improve efficiency. Computation results indicate that it reduces 76% simulation times by using OCBA.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究範圍 2
1.4 研究步驟與方法 2
第二章 文獻回顧 4
2.1 混合流程型生產排程問題 4
2.2 拆批分析 7
2.3 模擬最佳化 9
2.3.1 基因演算法(GA) 10
2.3.2 資源分配最佳化(OCBA) 12
第三章 考量批量分割之生產排程問題 16
3.1 問題定義 16
3.2 問題描述 17
3.2.1 訂單資訊 18
3.2.2 產品資訊 19
3.2.3 問題假設 20
3.2.4 求解目標 20
3.2.5 輸出資訊 20
3.3 數學模式 21
3.4 問題複雜度 21
3.5 研究方法論 27
3.5.1 基因演算法 28
3.6 模擬模式構建 34
3.6.1 模擬模式之範圍與細緻度 34
3.6.2 模擬工具 34
3.6.3 混合流程型生產系統模擬模式的建立 35
3.7 實驗設計與分析 37
3.7.1 三站別小型生產系統實驗 38
3.7.2 等量拆批實驗探討 42
3.7.3 變動拆批實驗探討 44
3.8 考量批量分割之生產排程結論 45
第四章 隨機環境下之批量分割生產排程問題 46
4.1 問題定義 46
4.1.1 探討加工時間具有隨機性 46
4.2 研究方法論 47
4.3 實驗設計與分析 50
4.3.1 OCBA與EQUAL比較 50
4.3.2 參數分析 53
4.4 隨機環境下之批量分割生產排程結論 56
第五章 結論與建議 57
5.1 結論 57
5.2 建議 58
參考文獻 59
附錄 63

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