Affordable experimental design with tens of variables

Simulation models have importantly expanded the analysis capabilities in engineering designs. With larger computing power, more variables can be modeled to estimate their effect in ever larger number of performance measures. Statistical experimental designs, however, are still somewhat focused on the variation of less than about a dozen variables. In this work, an effort to identify strategies to deal with tens of variables is undertaken. The aim is to be able to generate designs capable to estimate full quadratic models using simply a personal computer. Quadratic models are interesting because they can support statistical testing, provide competitive approximating models, and make optimization problems tractable. Several strategies are contrasted: (1) generate designs with random numbers, (2) use designs already available in the literature, (3) generate designs under a clustering strategy, and (4) generate designs using random-walk methods. The first strategy is an easy way to generate a design, although the statistical properties cannot be controlled. The second strategy does focus on statistical properties, but some of the designs become rapidly inconvenient to generate when increasing the number of variables. The third and fourth strategies are investigated as novel possibilities to generate designs in a convenient manner.