A0334
Title: Constructing two-level QB-optimal screening designs using mixed-integer programming and heuristic algorithms
Authors: Alan Vazquez - Tecnologico de Monterrey (Mexico) [presenting]
Peter Goos - Universiteit Antwerpen (Belgium)
WengKee Wong - UCLA (United States)
Abstract: Two-level screening designs are widely applied in the manufacturing industry to identify influential factors of a system. These designs have each factor at two levels and are traditionally constructed using standard algorithms, which rely on a pre-specified linear model. Since the assumed model may depart from the truth, two-level QB-optimal designs have been developed to provide efficient parameter estimates for several potential models. These designs also have an overarching goal, which is that models that are more likely to be the best for explaining the data are estimated more efficiently than the rest. However, there is no effective algorithm for constructing them. Two methods are presented: A mixed-integer programming algorithm that guarantees convergence to the two-level QB-optimal designs and a heuristic algorithm that employs a novel formula to find good designs in short computing times. Using numerical experiments, the mixed-integer programming algorithm is shown to be attractive for finding small optimal designs, and the heuristic algorithm is the most computationally-effective approach for constructing both small and large designs when compared to benchmark heuristic algorithms.
