Handling Optimization under Uncertainty Problem Using Robust Counterpart Methodology
DOI:
https://doi.org/10.9744/jti.15.2.111-118Keywords:
Optimization, uncertainty, conic, robust counterpartAbstract
In this paper we discuss the robust counterpart (RC) methodology to handle the optimization under uncertainty problem as proposed by Ben-Tal and Nemirovskii. This optimization methodology incorporates the uncertain data in U a so-called uncertainty set and replaces the uncertain problem by its so-called robust counterpart. We apply the RC approach to uncertain Conic Optimization (CO) problems, with special attention to robust linear optimization (RLO) problem and include a discussion on parametric uncertainty for that case. Some new supported examples are presented to give a clear description of the used of RC methodology theorem.References
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