Optimal designs for mixture-process experiments with control and noise variables

Peter J. Chung, Heidi B. Goldfarb, Douglas Montgomery

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Mixture experiments that involve process variables are widely encountered in industry. When some of the process variables are noise variables, interest focuses on finding levels for the mixture components and the controllable process variables that result in a product that has a desirable mean response and that has small variability in the response transmitted from the noise variables. This is a variation of the robust parameter design problem. Choosing an appropriate experimental design for this type of problem is addressed in the paper. We show how designs that have small prediction variance for the mean and the slope variance can be obtained. We also show how designs that are robust to the level of interaction between control and noise variables can be constructed.

Original languageEnglish (US)
Pages (from-to)179-190
Number of pages12
JournalJournal of Quality Technology
Issue number3
StatePublished - Jul 2007


  • Mixture-process experiment
  • Response surface methodology
  • Robust parameter design

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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