A lower bound for the correct subset-selection probability and its application to discrete-event system simulations

被引：109

作者：

Chen, CH

机构：

[1] Department of Systems Engineering, University of Pennsylvania, Philadelphia

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 1996年 / 41卷 / 08期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/9.533692

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Ordinal optimization concentrates on finding a subset of good designs, by approximately evaluating a parallel set of designs, and reduces the required simulation time dramatically for discrete-event simulation and optimization, The estimation of the confidence probability (CP) that the selected designs contain at least one good design is crucial to ordinal optimization. However, it is very difficult to estimate this probability in DES simulation, especially for complicated DES with large number of designs, This paper proposes two simple lower bounds for quantifying the confidence probability. Numerical testing will be presented.

引用

页码：1227 / 1231

页数：5

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