隨機最佳化係指在系統受隨機因子影響的情況下，找出最佳決策使系統有最好的表現，現今已有許多模擬最佳化演算法被提出以求解此類問題。過去，這些演算法皆是在假設模擬系統輸出是相互獨立的條件下發展而成的，然而，在現實中模擬輸出卻經常是彼此具有相關性的。在求解時，有時我們不希望所得到的實驗結果受到前面實驗結果干擾，且系統輸出之間若存在著相關性將會干擾輸入變數對系統帶來的真正影響。因此本研究基於在同一參數設定下連續蒐集得到之系統輸出具有自我相關性，且可視為一平穩序列的假設下，發展一自動化去相關流程，並結合STRONG演算法進行最佳化步驟，以排除相關性的干擾並得到最佳解，此演算法命名為STRONG–DP。

Simulation optimization has been widely used to solve stochastic optimization problems. The goal is to identify the system’s optimal parameter setting which leads to optimal performance. There have been many algorithms are proposed to solve the problems. In the past, the algorithms are developed under the assumption that outputs from a simulation system are independent. In reality, however, outputs are usually correlated with each other. Sometimes, when solving the optimization problem, we don’t want to be interfered by the previous experimental results. In addition, the effects on the system caused by input variables may be interfered by the correlation between outputs. In this study, under the assumptions that the consecutive outputs collected at the same parameter setting are autocorrelated and can be regarded as a stationary process, we propose an automated decorrelation procedure and combine with the STRONG algorithm to eliminate the correlation and get the optimal solution. The algorithm is called STRONG–DP.

摘要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 VII
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論文架構 3
第二章　文獻回顧 4
2.1時間序列方法 4
2.1.1 時間序列模型 4
2.1.2 模型選取準則 6
2.1.3 時間序列之預測區間 7
2.2 模擬最佳化 9
2.2.1 STRONG演算法 11
第三章　問題定義 19
第四章　求解方法 21
4.1去除系統輸出之間相關性 22
4.1.1自迴歸模型 22
4.1.2 模型選擇 23
4.1.3 自動化去相關流程 24
4.2 結合STRONG演算法 29
4.2.1 演算法架構 30
4.2.2 結合STRONG之去相關流程 32
4.2.3 內迴圈中心點之去相關流程 33
第五章　數值實驗 35
5.1自動化去相關流程 35
5.1.1績效指標 35
5.1.2預測區間檢測法成果 36
5.1.3自動化去相關成果 37
5.2演算法比較 39
5.2.1測試函數 39
5.2.2績效指標 42
5.2.3數值結果 43
第六章 實證研究 49
第七章 結論與未來研究 51
參考文獻 52

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