生活中的問題隨著科技發展越來越複雜，當問題難以使用數學模式分析時，利用系統模擬的技術，能夠放寬許多不合理假設以建立模型幫助決策。模擬領域中的排序與選擇程序，在問題的候選解不多時，幫助決策者選擇最佳系統。而排序與選擇程序在估計變異較大的系統績效表現值時，容易面臨抽樣過多及運算時間長等問題；模擬領域中的變異縮減技術則可改善此問題，以變異數較小的估計量取代原本的平均數估計量，降低平均抽樣數，增加完全連續選擇程序之效率。在過去文獻中，以分位數作為選擇指標的選擇程序多為兩階段的選擇程序，且皆需要面對分位數估計量具有較大偏誤，或是該估計量不為常態分配等問題，且當欲估計的分位數為極端分位數時，因有效的樣本不易取得，同樣也面臨抽樣數過高等問題。本研究使用分位數的漸進性質並將其引入完全選擇程序中，且在估計極
端分位數的例子中使用重要性取樣法降低變異，幫助程序減少抽樣數。
在本研究中使用Bahadur-Ghosh representation 計算分位數的漸進變異數，並利用其中央極限定理將分位數估計量使用於完全連續程序中；接著使用模擬最佳化中的樣本平均近似法估計重要性取樣法的參數 ，利用當前程序下產生的樣本定義分位數的漸進變異數，將其表示為 的函數，將其作為最佳化的目標式進行求解，最後將使用重要性取樣法的分位數估計量導入完全連續選擇程序中。
經由實驗發現本研究提出之FSP-CMC ，其平均抽樣數比過往文獻更少，並同時能符合信心水準。而導入了重要性取樣法的FSP-IS 程序則在估計極端分位數的實驗中取得了良好的變異減免效果，其所需要的樣本數遠低於FSP-CMC 程序。

In past studies, Ranking and Selection Procedures (R&S) considering quantile usually use sample mean and variance as the point estimators. These procedures may not be efficient in terms of the average number of samples since quantile estimator has large variance and bias, especially when extreme quantiles are under consideration.
We propose a Fully Sequential Selection Procedure (FSP) algorithm with asymptotic variance of the quantile estimator, which do not need separate samples into batches.
This procedure preforms well since the point estimator has low bias. Moreover, we propose a FSP with Importance Sampling (IS) that updates the IS parameter when we take more samples. To update the parameter, we use stochastic optimization approach, which is Sample Average opproximation (SAA). The numerical experiments indicate that the probability of correct selection (PCS) can meet the desired confidence level, and variance reduction is achieved since average number of samples (ANS) is significantly decreased.

蔡承濂(2017). 以更有效率之方式結合控制變量於完全連續選擇程序. 成功大學工業與資訊管理學系碩士學位論文.
葉韋呈(2019). 使用非線性控制變量之完全連續選擇程序. 成功大學工業與資訊管理學系碩士學位論文.
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