.png){kind=icon}
返回
Generating and improving orthogonal designs by using mixed integer programming
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2011.07.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Analysts faced with conducting experiments involving quantitative factors have a variety of potential designs in their portfolio. However, in many experimental settings involving discrete-valued factors (particularly if the factors do not all have the same number of levels), none of these designs are suitable. In this paper, we present a mixed integer programming (MIP) method that is suitable for constructing orthogonal designs, or improving existing orthogonal arrays. for experiments involving quantitative factors with limited numbers of levels of interest. Our formulation makes use of a novel linearization of the correlation calculation. The orthogonal designs we construct do not satisfy the definition of an orthogonal array, so we do not advocate their use for qualitative factors. However, they do allow analysts to study, without sacrificing balance or orthogonality, a greater number of quantitative factors than it is possible to do with orthogonal arrays which have the same number of runs. (C) 2011 Elsevier B.V. All rights reserved.
Keyword:
Orthogonal design creation
Design of experiments
Statistics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
引用论文
分享收藏分享收藏
Key comparison BIPM.RI(I)-K4 of the absorbed dose to water standards of the GUM, Poland and the BIPM in 60Co gamma radiation.png)
Metrologia
MetrologiaIF0
PREAI
分享收藏
查看更多内容