| Summary: | 碩士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 98 === 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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