隨機系統模擬是目前廣泛使用的技術之一，在現實生活中可以應用在很多領域，然而，處理系統之隨機性本身是一相當困難之問題，對於大型隨機系統，其困難度更是大幅增加。由於模擬模型的建立需要非常接近真實系統，即使現代電腦運算資源發達，若要針對大規模問題進行最佳化，成千上百的因子所需花費的模擬資源仍相當可觀，因此，篩選因子方法常被用於最佳化方法前來找出重要因子減少其求解成本。目前已有許多篩選因子方法及最佳化方法可供選擇，然而，典型的模擬最佳化方法是將問題視為一個隨機系統並以期望值作為績效指標衡量，相較於期望值，分量可以適用於ㄧ些期望值不適用的問題中，卻少有以分量作為績效衡量指標的研究。因此本研究提出一個有效率的模擬最佳化架構，透過分量式的篩選因子方法搭配分量式最佳化方法來進行模擬最佳化，稱其為STRONG-Q，STRONG-Q是以CSB及STRONG作修改，使其可以處理目標式為分量的問題，在篩選因子方法中，透過型一誤差及檢定力的控制，可確保重要因子能被篩選中且不重要因子不被篩選中，使後續的最佳化步驟能正確且有效率地進行。

Screening experiments are often conducted before optimization in order to reduce computation resources by identifying the important factors of the problem. In the literatures, factor screening and simulation optimization approaches mostly adopted expectation as performance measures. The methodologies that are focused on other alternatives, however, are difficult to develop due to a lack of nice statistical properties as expectation. Quantile is an important alternative to the expectation for spatial data and moreover, it enables risk control. In this study, we propose a novel approach called STRONG-Q that integrates efficient quantile-based factor screening methods into the framework of STRONG, which is a newly-developed Response-Surface-based framework, for large-scale quantile-based simulation optimization problems. The quantile-based factor screening method can effectively control the Type I error and enables the large-scale quantile-based simulation optimization problems to be solved efficiently when it is integrated into STRONG.

摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VI
第一章 緒論 2
1.1 研究背景與動機 2
1.2 研究目的 4
1.3 論文架構 4
第二章 文獻探討 6
2.1 篩選因子方法 6
2.2 分量估計方法 10
2.3 模擬最佳化方法 10
第三章 問題定義 15
3.1 分量式篩選因子 15
3.2 分量式最佳化 17
第四章 STRONG-Q演算法 18
4.1 STRONG-Q之篩選因子架構 19
4.1.1 篩選因子實驗 19
4.1.2 分量估計 21
4.1.3 篩選因子流程 22
4.1.4 漸進式分量因子篩選 27
4.2 STRONG-Q之最佳化架構 29
4.2.1 最佳化架構 29
4.2.2 樣本數與設計點 31
4.2.3 Stage Ι 32
4.2.4 Stage ΙΙ 35
第五章 數值實驗 39
5.1 篩選因子正確性 39
5.1.1 數值模型 39
5.1.2 篩選結果 41
5.2 演算法比較 44
5.2.1 測試函數 44
5.2.2 績效指標 45
5.2.3 數值結果 46
第六章 實證研究 50
6.1 送報生問題 (News Vendor Problem) 50
6.2 多產品組裝生產問題 (Multiproduct Assembly) 52
第七章 結論與未來研究 54
參考文獻 55

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