Taguchi's parameter design: a panel discussion
Technometrics
Development of a groundwater level forecasting system for the optimal operation of a groundwater dam
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
A surrogate variable-based data mining method using CFS and RSM
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Development of a robust data mining method using CBFS and RSM
PSI'06 Proceedings of the 6th international Andrei Ershov memorial conference on Perspectives of systems informatics
Development of a job stress evaluation methodology using data mining and RSM
ICOST'07 Proceedings of the 5th international conference on Smart homes and health telematics
Computing trade-offs in robust design: Perspectives of the mean squared error
Computers and Industrial Engineering
Development of a sequential robust-tolerance design model for a destructive quality characteristic
Computers and Industrial Engineering
Development of a data mining methodology using robust design
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
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 @e-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.