Generating experimental designs involving controland noise variables using genetic algorithms

Myrta Rodriguez, Douglas Montgomery, Connie M. Borror

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1045-1065
Number of pages21
JournalQuality and Reliability Engineering International
Volume25
Issue number8
DOIs
StatePublished - Dec 1 2009

Keywords

  • Alphabetic optimality
  • Fraction of design space plots
  • Genetic algorithms
  • Prediction variance
  • Robust design

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

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