Matrix analysis
Empirical model-building and response surface
Empirical model-building and response surface
Taguchi's parameter design: a panel discussion
Technometrics
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Bias-specified robust design optimization: A generalized mean squared error approach
Computers and Industrial Engineering
Computers and Industrial Engineering
Development of an adaptive response surface method for optimization of computation-intensive models
Computers and Industrial Engineering
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Robust design has received consistent attention from researchers and practitioners for years, and a number of methodologies for robust design optimization have been reported in the research community. However, the majority of these existing methodologies ignore the case where the customer may tolerate and specify an upper bound on process bias. This paper proposes a bias-specified robust design method and formulates a nonlinear program that minimizes process variability subject to customer-specified constraints on the process bias using the ε-constrained method. This paper then derives the Karush-Khun-Tucker conditions and provides a solution procedure based on the Lagrangean method. A numerical example is provided for illustration.