Design of experiment is an efficient statistical methodology of establishing which input variables are important (have significant effects) in an experiment (process) and the conditions under which these inputs should work to optimize the outputs of that process. Two-level designs are widely used in high-tech industries and manufacturing for productivity and quality improvement experiments. The construction of (nearly) optimal two-level designs for real-life experiments with large number of input variables can be quite challenging. The practice demonstrated that the existing techniques are complex, highly time-consuming, produce limited types of designs, and likely to fail in large experiments (i.e., optimal results are not expected). To overcome these significant problems, this article gives a simple and effective technique for constructing large two-level designs with good statistical properties. To meet practical needs in different fields, the statistical properties of the generated designs by the new technique are investigated from four basic perspectives: minimizing the similarity among the experimental runs, minimizing the aliasing among the input variables, maximizing the resolution, and filling the experimental domain as uniformly as possible. New recommended saturated orthogonal main effect plans and uniform orthogonal arrays of strength three with thousands or even millions of runs and factors are generated via the new technique without recourse to optimization software.

实验设计是一种有效的统计方法，可以确定哪些输入变量在实验（过程）中是重要的（具有显着影响）以及这些输入应在哪些条件下工作以优化该过程的输出。两级设计广泛用于高科技行业和制造业，以进行生产率和质量改善实验。对于具有大量输入变量的实际实验，（几乎）最佳的两级设计的构造可能非常具有挑战性。实践证明，现有技术复杂，耗时，设计类型有限，并且可能无法在大型实验中失败（即，无法预期获得最佳结果）。为了克服这些重大问题，本文为构建具有良好统计特性的大型两级设计提供了一种简单有效的技术。为了满足不同领域的实际需求，从四个基本角度研究了通过新技术生成的设计的统计属性：最小化实验运行之间的相似性，最小化输入变量之间的混叠，最大化分辨率和填充实验域尽可能统一。通过新技术可以生成新的推荐饱和正交主效应计划以及强度三的均匀正交数组，这些函数具有数千甚至上百万个游程和因子，而无需借助优化软件。从四个基本角度研究了通过新技术生成的设计的统计属性：最小化实验运行之间的相似性，最小化输入变量之间的混叠，最大化分辨率以及尽可能均匀地填充实验域。通过新技术可以生成新的推荐饱和正交主效应计划以及强度三的均匀正交数组，这些函数具有数千甚至上百万个游程和因子，而无需借助优化软件。从四个基本角度研究了通过新技术生成的设计的统计属性：最小化实验运行之间的相似性，最小化输入变量之间的混叠，最大化分辨率以及尽可能均匀地填充实验域。通过新技术可以生成新的推荐饱和正交主效应计划以及强度三的均匀正交数组，这些函数具有数千甚至上百万个游程和因子，而无需借助优化软件。