Grant: Discovery Grants Program - Individual

This research is concerned with obtaining optimal designs for use in industrial and computer experimentation. Industrial screening experiments are frequently used to ascertain which factors play a statistically significant role in the quality of a product or process under study. It is often the case that the levels of some of the experimental factors are more difficult, or expensive, to vary than the rest. In such settings "split-plot designs" often arise. This research considers the optimal choice of split-plot designs consisting of only a few runs (for example, 12, 16, 20, 24, etc). Such designs offer practitioners more flexible run sizes than most split-plot designs presently cataloged in the literature and as such should result in cost-savings for industrial experimenters. In recent years, the search for optimal designs for use in industrial experimentation has expanded to the development of optimal designs for use in "computer experiments". Computer experiments are useful in situations where experimentation is physically impossible (or prohibitively expensive or time-consuming). In such settings complex mathematical models, implemented via computer code, may be developed to mimic the physical process under study for a fraction of the cost. Such models may consist of dozens of parameters, and the output from the computer experiment may consist of thousands of observations. This research considers the design of computer experiments in the setting where functional output occurs. Furthermore, in this context, the effect of "noise", or uncontrollable factors, is also considered in the choice of optimal factor level settings for computer experiments.