Large Factorial Designs for Product Engineering and Marketing Research Applications

Abstract

We propose an algorithm that incorporates both combinatorial and heuristic optimization methods for generating D-efficient factorial designs. This includes an approach that can automatically construct more than 115,000 orthogonal arrays, including at least one array for every known specification with fewer than 144 runs. The algorithm tries various initialization and iteration methods, chooses the most promising method, and then applies more computational effort with the chosen method. The algorithm can make orthogonal and nearly-orthogonal arrays, and it can accommodate restrictions, interactions, and large designs. Usage requires minimal expertise and input. Applications in the area of optimal product design, choice modeling, and marketing research are discussed.

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