The uncertainty of the model form is typically neglected in process-optimization studies. In addition, not taking into account the existence of noise factors and nonnormal errors may invalidate the conclusions of such studies. In this paper, a Bayesian approach to model-robust process optimization in the presence of noise factors and nonnormal error terms is presented. Traditionally, in process optimization, methods such as the dual response approach are used in the presence of noise factors, and methods such as robust regression are used when the error terms are not normally distributed. Instead, this paper extends the recently proposed idea of model form-robustness using a Bayesian predictive approach to cases where there is uncertainty due to noise factors and due to the distributional assumptions of the errors. Two examples taken from the literature, one based on a factorial experiment and another based on a mixture experiment, are used to illustrate the proposed approach.
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering