Abstract
The study introduced an algorithm for generating optimal split-plot designs. The designs were considered as optimal because they were capable and ecient in estimating the xed e ects of the statistical model that is appropriate given the split-plot design structure. Here, we introduced I-optimal design of split-plot experiments. The algorithm used in this research does not require the prior speci cation of a candidate set. Therefore, making the design of split-plot experiments computationally feasible in situations where the candidate set is too large to be tractable. Flexible choice of the sample size, inclusion of both continuous and categorical factors were allowed by this method. We show through an example the substantial bene ts of this method.
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