Title: No-confounding designs with 24 runs for 7-12 factors

Authors: Brian B. Stone; Douglas C. Montgomery; Rachel T. Silvestrini; Bradley Jones

Addresses: Department of Operational Sciences, Air Force Institute of Technology (AFIT), 2950 Hobson Way, WPAFB, OH 45433, USA ' Department of Industrial Engineering, Arizona State University, 699 S. Mill Avenue, Phoenix, AZ 85281, USA ' Department of Industrial and Systems Engineering, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY 14623-5603, USA ' SAS Institute, SAS Campus Drive, Building S, Cary, NC 27513, USA

Abstract: When experimenting with more than six independent variables, researchers under significant resource or time constraints often require alternatives to large Resolution V 2k-p fractional factorial designs. Many researchers also desire to avoid the expense of a foldover experiment required to de-alias completely confounded two-factor interactions (2FIs) when using resolution IV 2k-p designs. No-confounding designs are an excellent solution to this problem, as they have orthogonal main effects (ME) and no 2FI is completely confounded with another ME or 2FI. This paper introduces 24-run no-confounding designs for 7-12 factors. It presents a Monte Carlo simulation methodology used to evaluate algorithmically constructed designs and those in the existing literature. The results report the best-performing designs and metrics related to their types I and II error rate from the variable-selection process during repeated simulations of regression analyses.

Keywords: experimental design; Monte Carlo simulation; no-confounding designs; nonregular designs; orthogonal arrays.

DOI: 10.1504/IJEDPO.2017.087583

International Journal of Experimental Design and Process Optimisation, 2017 Vol.5 No.3, pp.151 - 171

Received: 04 Oct 2016
Accepted: 25 Mar 2017

Published online: 21 Oct 2017 *

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