Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Development of robust design optimization using incomplete data
Computers and Industrial Engineering
Bias-specified robust design optimization: A generalized mean squared error approach
Computers and Industrial Engineering
Multiresponse optimization of dispatch rules for public bus services
Computers and Industrial Engineering
Robust design of nonlinear multiple dynamic quality characteristics
Computers and Industrial Engineering
A robust parameter design for multi-response problems
Journal of Computational and Applied Mathematics
Computers and Industrial Engineering
Optimization of a multi-response problem in Taguchi's dynamic system
Computers and Industrial Engineering
Bias-specified robust design optimization and its analytical solutions
Computers and Industrial Engineering - Special issue: Selected papers from the 31st international conference on computers & industrial engineering
A review of optimization techniques in metal cutting processes
Computers and Industrial Engineering
Robust optimization for multiple responses using response surface methodology
Applied Stochastic Models in Business and Industry
Constraint handling with modified hypervolume indicator for multi-objective optimization problems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
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One technique used frequently among quality practitioners seeking solutions to multi-response optimization problems is the desirability function approach. The technique involves modeling each characteristic using response surface designs and then transforming the characteristics into a single performance measure. The traditional procedure, however, calls for estimating only the mean response; the variability among the characteristics is not considered. Furthermore, the approach typically relies on the accuracy of second-order polynomials in its estimation, which are not always suitable. This paper, in contrast, proposes a methodology that utilizes higher-order estimation techniques and incorporates the concepts of robust design to account for process variability. Several examples are provided to illustrate the effectiveness of the proposed methodology.