Optimal splitk-plot designs

Completely randomized designs are often infeasible due to the hard-to-change nature of one or more experimental factors. In those cases, restrictions are imposed on the order of the experimental tests. The resulting experimental designs are often split-plot or split-split-plot designs in which the levels of certain hard-to-change factors are varied only a limited number of times. In agricultural machinery optimization, the number of hard-to-change factors is so large and the available time for experimentation is so short that split-plot or split-split-plot designs are infeasible as well. The only feasible kinds of designs are generalizations of split-split-plot designs, which are referred to as split^k-designs, where k is larger than 2. The coordinate-exchange algorithm is extended to construct optimal split^k-plot designs and the added value of the algorithm is demonstrated by applying it to an experiment involving a self propelled forage harvester. The optimal design generated using the extended algorithm is substantially more efficient than the design that was actually used. Update formulas for the determinant and the inverse of the information matrix speed up the coordinate-exchange algorithm, making it feasible for large designs.