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Large, complex computer simulation models can require prohibitively costly and time-consuming experimental programs to study their behavior. Therefore we may want to concentrate the analysis on the set of “most important” factors (i.e., input variables). Factor screening experiments, which attempt to identify the more important variables, can be extremely useful in the study of such models. The number of computer runs available for screening, however, is usually severely limited. In fact, the number of factors often exceeds the number of available runs. In this paper we present a survey of supersaturated designs for use in factor screening experiments. The designs considered are: random balance, systematic supersaturated, group screening, modified group screening, T-optimal, R-optimal, and search designs. We discuss in general terms the basic technique, advantages, and disadvantages of each procedure surveyed.

Efficient identification of important factors in large scale simulations