The search for experimental design with tens of variables: Preliminary results

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. Several strategies are contrasted: (i) generate designs with random numbers, (ii) use designs already in the literature, and (iii) generate designs under a clustering strategy. The first strategy is an easy way to generate a design. The second strategy does focus on statistical properties, but the designs become somewhat inconvenient to generate when increasing the number of variables. The third strategy is currently being investigated as a possibility to provide a balance between (i) and (ii).