The OPTEX procedure searches for optimal experimental designs. You specify a set of candidate design points and a linear model, and the procedure chooses points so that the terms in the model can be estimated as efficiently as possible. Most experimental situations call for standard designs, such as fractional factorials, orthogonal arrays, central composite designs, or Box-Behnken designs. Standard designs have assured degrees of precision and orthogonality that are important for the exploratory nature of experimentation. In some situations, however, standard designs are not available, such as when: not all combinations of the factor levels are feasible; the region of experimentation is irregularly shaped; resource limitations restrict the number of experiments that can be performed; there is a nonstandard linear or a nonlinear model. The OPTEX procedure can generate an efficient experimental design for any of these situations.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Piepho, Hans-Peter; Williams, Emlyn R.; Michel, Volker: Nonresolvable row-column designs with an even distribution of treatment replications (2016)
- Sirisom, P.; Chaimongkol, S.; Borkowski, J.J.: Using genetic algorithms to generate $D_s$-optimal response surface designs (2014)
- Thongsook, S.; Budsaba, K.; Borkowski, J.J.: Using a genetic algorithm to generate $D_s$-optimal designs for mixture experiments in a simplex region (2014)
Further publications can be found at: http://support.sas.com/documentation/cdl/en/qcug/63922/HTML/default/viewer.htm#qcug_optex_sect001.htm