Document Type

Article

Publication Date

January 2013

Publication Title

Computers & Industrial Engineering

Volume

66

Issue Number

3

First Page

601

Last Page

613

DOI

10.1016/j.cie.2013.08.005

Keywords

Design of experiment, Two-level factorial design, Sequential design, Resolution, Optimal design, Run minimization

Disciplines

Industrial Engineering | Operations Research, Systems Engineering and Industrial Engineering | Systems Engineering

Abstract

2k Full factorial designs may be prohibitively expensive when the number of factors k is large. The most popular technique developed to reduce the number of treatment combinations is the fractional factorial design; confounding in estimating the model parameters naturally results in various resolution and aberration levels. While very useful, these resolution levels may not satisfy experimenters’ requirements for estimatibility and cost reduction. For example, while Resolution V ensures a common requirement that no two-factor interactions are confounded, it also imposes an often undesired restriction that a main effect cannot be confounded with a three-factor interaction, which may very well be non-existent or negligible. We propose a new concept of “active confounding avoidance” whose goal is to identify, for any given set of parameters, a set of treatment combinations such that estimates of the parameters are not confounded with one another. We show that the “least-treatment-combinations” methods developed for identifying a minimal set of m treatment combinations for a 2kmodel with only m non-zero parameters achieve this goal. We then propose a simple design pattern that achieves active confounding avoidance for parameters spanning from all main effects up to all i-factor interactions, for all i = 1, 2, … , k, all with the least number of treatment combinations. The pattern also specifies how treatment combinations should be sequenced for experimentation according to a parameter sequence of decreasing magnitude possibly specified by the experimenter based on prior knowledge. The former sequence is optimal in that the experimentation can stop whenever the current model is deemed adequate and experiments already conducted could be considered necessary.

Comments

This is the Author's Original Manuscript of an article that appeared in Computers & Industrial Engineering, 66, 3, 2013. The Version of Record is available at the following link: http://doi.org/10.1016/j.cie.2013.08.005.
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