Ample Discussions on Robust Design Modeling and Optimization

Robust design (RD) based on Taguchi’s philosophy is to improve the quality of products and processes by reducing noise factors’ effects. For more than two decades, a number of RD methods have been received constant attentions from many researchers and practitioners. In this paper, ample discussions for RD methods are demonstrated by categorizing three RD stages, such as experimental design, estimation, and optimization. In experimental design stage, a number of experiment formats associated with control and noise factors are discussed in order to investigate existing problems and further research directions. Also, many RD estimation methods (i.e., response surface methodology (RSM), maximum likelihood estimation (MLE), and Bayesian approach) reported in literature are introduced. Finally, analyses of historical and functional development of optimization models for single and multiple responses problems are summarized in this paper.