系統模擬已廣泛運用於今日社會中，舉凡是各種人類活動皆可應用於模擬領域中。其中，排序與選擇程序之目的是從多個系統中，在信心水準下推薦具有競爭力之系統，提供使用者決策參考；然而，系統本身之輸出值變異數較大，將會導致效率較差，造成時間與成本之浪費。因此，藉由變異減免技術的發展，在統計的理論基礎下，建立新的估計量取代原本之樣本平均數，使變異數下降，提升各程序之效率。控制變量係以線性迴歸的手法，解釋輸出值之誤差，建立控制估計量取代原本的樣本變異數，且控制變量運用於排序與選擇程序已相當成熟。而相關性導入技術係利用在產生隨機變數時，藉由控制隨機亂數，使同一系統中之輸出值彼此具有負相關性，以達到減少變異數之目的。

本研究建立四種結合控制變量與相關性導入之模型，並運用其估計量於篩選程序、兩階段選擇程序、多階段選擇程序與完全連續選擇程序，且證明或推論程序符合信心水準，並分析何種情況下合併程序優於獨立程序。透過實驗，本研究各程序皆可滿足信心水準，且當面對的問題越困難時，帶來之好處也越大，所需樣本較低。並從實驗數據中發現兩階段選擇程序最容易藉由導入負相關，使效率下降，但該程序所需樣本卻最多；多階段選擇程序效率最佳，然而卻最不易得到負相關所帶來之好處；完全連續選擇程序則介於兩者之間。本研究建議各程序在解決不同問題時使用不同之模型。對於篩選程序，建議使用
Model 3 於樣本數與控制值個數較接近問題；Model 4用於解決複雜問題並設定較大初始樣本配合執行之；其餘情況使用 Model 1。而多階段選擇程序與完全連續選擇程序，在較困難問題中，建議使用Model 1之方法並配合對偶變量；較容易之問題則使用獨立控制變量程序。而若使用兩階段選擇程序則不論問題難度皆建議使用本研究提出之Model 1 方法。

The propose of using Ranking and Selection Procedures is to find
superior systems form all of candidate systems. However, if the
variance of system output is large, we need to sampling more sample
to find best system to guarantee confidence level. Therefore, we
apply Variance Reduction Technique to procedures, replacing the
origin estimator, sample mean, to accomplish the propose of reducing
variance; and further, decrease the number of samples we need.

In our research, we establish four model of combining Correlation
Induction and Control Variates in Screening Procedure, Multistage
Selection Procedure, Two-stage Selection Procedure, and Fully
Sequential Selection Procedure, and by inference or proving that
each procedure will guarantee confidence level, and we also analyze
in what condition our combine procedure will be better than CV
procedure. Through empirical results and a realistic illustration,
we find that the probability of correct selection of all procedures
will conform to confidence level guarantee, and when the problem is
more complicated, then we can get more benefit form our combine
procedures. In the end of our research we conclude that, for
Screening Procedure, we recommend using Model 3 when the number of
samples and the number of controls is close; using Model 4 when the
problem is complex with setting large samples to do; in the other
condition we suggest using Model 1. For Multistage Selection
Procedure and Fully Sequential Selection Procedure, we recommend
using Model 1 when facing a complicated problem, but otherwise using
the CV procedure. Finally, for Two-stage Procedure we recommend
using Model 1 in all situations.

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