Integrated multi-objective robust optimization and sensitivity analysis with irreducible and reducible interval uncertainty

Abstract

Uncertainty considered in robust optimization is usually treated as irreducible since it is not reduced during an optimization procedure. In contrast, uncertainty considered in sensitivity analysis is treated as partially or fully reducible in order to quantify the effect of input uncertainty on the outputs of the system. Considering this, and the usual existence of both reducible and irreducible uncertainty, an approach that can perform robust optimization and sensitivity analysis simultaneously is of much interest. This article presents such an integrated optimization model that can be used for both robust optimization and sensitivity analysis for problems that have irreducible and reducible interval uncertainty, multiple objective functions and mixed continuous-discrete design variables. The proposed model is demonstrated by two engineering examples with differing complexity to demonstrate its applicability.

Keywords:

• robust optimization
• sensitivity analysis
• uncertainty
• multi-objective optimization

Acknowledgements

The work presented in this article was supported in part by the Office of Naval Research Grant # N000140810384. It was also supported in part by The Petroleum Institute (PI), Abu Dhabi, United Arab Emirates, as part of the Education and Energy Research Collaboration (EERC) agreement between the PI and University of Maryland, College Park. Such support does not constitute an endorsement by the funding agency of the opinions expressed in the article. The software, ATSV, developed by the Applied Research Laboratory (ARL) of Penn State University (Stump et al. 2004) was used in the article to plot the correlation figures.