Abstract
In this study, a new optimization approach for robust design, design for multi-objective six sigma, has been developed and applied to three robust optimization problems.The design for multi-objective six sigma builds on the ideas of design for six sigma, coupled with multiobjective evolutionary algorithm, for an enhanced capability to reveal tradeoff information considering both optimality and robustness of design. While design for six sigma requires careful input parameter setting, design for multi-objective six sigma needs no such prior tuning, plus it can reveal the tradeoff information in a single optimization run. Three robust optimization problems were taken as to demonstrate the capabilities of design for multiobjective six sigma. Results indicate that design for multi-objective six sigma has a more practical and more efficient capability than the design for six sigma to reveal tradeoff design information considering both optimality and robustness of design.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Aerospace Computing, Information, and Communication
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
