Supersaturated designs that maximize the probability of identifying active factors

Supersaturated designs and associated analysis methods have been proposed by several authors to identify active factors in situations in which only a very limited number of experimental runs is available. We use simulation to evaluate the abilities of the existing methods to achieve model identification-related objectives. The results motivate a new class of supersaturated designs, derived from simulation optimization, that maximize the probability that stepwise regression will identify the important main effects. Because the proposed designs depend on specific assumptions. we also investigate the sensitivity of the performances of the alternative supersaturated designs to these assumptions.