Author:

Keywords:

Cost, D- and I-Optimality Criterion, Response Surface Model, Split-Plot Design, Split-Split-Plot Design, Staggered-Level Design, Science & Technology, Technology, Physical Sciences, Engineering, Industrial, Operations Research & Management Science, Statistics & Probability, Engineering, Mathematics, SPLIT-PLOT EXPERIMENTS, CONSTRUCTION, ALGORITHM, 0104 Statistics, 0913 Mechanical Engineering, 1503 Business and Management, 3507 Strategy, management and organisational behaviour, 4905 Statistics

Abstract:

In industrial experiments, there are often restrictions in randomization caused by equipment and resource constraints, as well as budget and time constraints. Next to the split-plot and the split-split-plot design, the staggered-level design is an interesting design option for experiments involving two hard-to-change factors. The staggered-level design allows both hard-to-change factors to be reset at di?erent points in time, resulting in a typical staggering pattern of factor-level resettings. It has been shown that, for twolevel designs, this staggering pattern leads to statistical benefits in comparison to the split-plot and the split-split-plot design. In this paper, we investigate whether the benefits of the staggered-level design carry over to situations where the objective is to optimize a response and where a second-order response surface model is in place. To this end, we study several examples of D- and I-optimal staggered-level response surface designs.