Generating experimental designs involving control and noise variables using genetic algorithms
Article first published online: 3 APR 2009
Copyright © 2009 John Wiley & Sons, Ltd.
Quality and Reliability Engineering International
Volume 25, Issue 8, pages 1045–1065, December 2009
How to Cite
Rodriguez, M., Montgomery, D. C. and Borror, C. M. (2009), Generating experimental designs involving control and noise variables using genetic algorithms. Qual. Reliab. Engng. Int., 25: 1045–1065. doi: 10.1002/qre.1020
- Issue published online: 16 NOV 2009
- Article first published online: 3 APR 2009
- genetic algorithms;
- robust design;
- prediction variance;
- fraction of design space plots;
- alphabetic optimality
Efficient estimation of response variables in a process is an important problem that requires experimental designs appropriated for each specific situation. When we have a system involving control and noise variables, we are often interested in the simultaneous optimization of the prediction variance of the mean (PVM) and the prediction variance of the slope (PVS). The goal of this simultaneous optimization is to construct designs that will result in the efficient estimation of important parameters. We construct new computer-generated designs using a desirability function by transforming PVM and PVS into one desirability value that can be optimized using a genetic algorithm. Fraction of design space (FDS) plots are used to evaluate the new designs and six cases are discussed to illustrate the procedure. Copyright © 2009 John Wiley & Sons, Ltd.