The purpose of this project is to develop new cost-efficient experimental plans that yield more and better information and thus enable faster innovation in the presence of factors with hardto-change levels. To this end, we use the new family of staggered-level experimental designs. We develop construction methods for staggered-level designs with any number of hard-tochange
factors. A novel feature of our approach is that, unlike published work on the design of split-(split-)plot experiments, it aims at a precise estimation of all factor effects as well as at a precise estimation of the variance components in the estimated model. To this end, we apply state-of-the-art combinatorial optimization techniques to the design of experiments.