Title: Fractional factorial designs that maximise the probability of identifying the important factors
Authors: Theodore T. Allen, Navara Chantarat, Cenny Taslim
Addresses: The Ohio State University, 210 Baker Systems, 1971 Neil Avenue, Columbus, OH 43210, USA. ' Prince of Songkla University, P.O. Box 38 Khohong, Hat Yai, Songkhla, 90112, Thailand. ' The Ohio State University, 210 Baker Systems, 1971 Neil Avenue, Columbus, OH 43210, USA
Abstract: We use simulation to evaluate the abilities of fractional factorial designs and associated analysis methods to achieve model identification-related objectives. We show that these simulations can provide potentially useful insights to decision makers before experimentation begins. Findings include that Type II error rates might be higher than is commonly realised. Also, we propose new balanced and unbalanced fractional factorial designs, derived from simulation optimisation, that maximise the probability of correct selection of which factors are important and which are unimportant.
Keywords: design of experiments; DOE; screening experiments; simulation optimisation; fractional factorial design; model identification.
International Journal of Industrial and Systems Engineering, 2009 Vol.4 No.2, pp.133 - 150
Published online: 02 Jan 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article