Augmenting supersaturated designs with Bayesian D-optimality

A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D-optimality criterion to increase the information gained about those factors. Simulation results show the method can outperform existing supersaturated design augmentation strategies that add runs without analyzing the initial response variables. (C) 2013 Elsevier B.V. All rights reserved.