Optimal sequencing of test conditions in 2^sup k^ factorial experimental design for run-size minimization

The exponential growth of the number of test conditions (i.e., the "run size") of a 2... factorial design makes the design prohibitively expensive for a large k. When only m of the 2... effects/interactions are non-zero, only m test conditions are required for their estimation. However, bo...

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Bibliographic Details
Published inComputers & industrial engineering Vol. 55; no. 2; p. 450
Main Authors Tsao, H-S Jacob, Liu, Hongrui
Format Journal Article
LanguageEnglish
Published New York Pergamon Press Inc 01.09.2008
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Summary:The exponential growth of the number of test conditions (i.e., the "run size") of a 2... factorial design makes the design prohibitively expensive for a large k. When only m of the 2... effects/interactions are non-zero, only m test conditions are required for their estimation. However, both fractional factorial design and Taguchi method require 2n test conditions, for some n ... k, and therefore may require more test conditions than necessary. Given the identities of the m non-zero effects/interactions, Tsao and Wibowo recently developed an algorithm to identify a set of exactly m test conditions but did not suggest how to test the adequacy of the m-unknown model or how to expand the set of test conditions incrementally when more non-zero effects/interactions actually exist. This paper proposes to incrementally and efficiently expand the model by developing an effect-interaction sequence in the descending order of their magnitudes. Given any such sequence, we provide a simple algorithm to sequence the 2... test conditions so that, for any m, 1 ... m ... 2..., the first m effects/interactions in the effect-interaction sequence can be estimated with exactly the first m test conditions in the corresponding test-condition sequence and no more, if all the other 2... - m effects/interactions are zero. A benefit of this is that experiments can be performed sequentially according to the test-condition sequence until the first insignificant effect/interaction is found. The proposed method can also be used for situations where knowledge about the effects/interactions is too vague to sort them according to their magnitudes. (ProQuest: ... denotes formulae/symbols omitted.)
ISSN:0360-8352
1879-0550