Mathematical Sciences: Topics in Economical Experimental Designs

这项研究考察了一阶饱和、过饱和和最小水平变化设计在最小化区分影响因素和仅造成噪声的影响因素所需的实验次数问题中的作用。工业环境中的最优实验设计也可能以最小化实验次数为目标。理论上的解决方案往往依赖于对可能的影响因素数量及其相互作用的不切实际的假设。这项研究的重点是一种高效有效的设计最小系列实验的方法，以确定这些因素，目的是预测全面生产所需的因素或因素设置的最佳组合。

This research examines the roles of first-order saturated, supersaturated and minimal level change designs in the problem of minimizing the number of experiments required to differentiate the influential factors from those only contributing noise. Optimal experimental design in an industrial setting may also have as an objective the minimization of the number of experiments. Theoretical solutions have often depended upon unrealistic assumptions about the likely number of influential factors and their interactions. This research focuses on an efficient and effective approach to the design of a minimal series of experiments to identify those factors with the goal of predicting the optimal combination of factors or factor settings for full-scale production.