Handling Optimization under Uncertainty Problem Using Robust Counterpart Methodology


  • Diah Chaerani Faculty of Mathematics and Natural Sciences, Department of Mathematik, Universitas Padjadjaran, Jl. Raya Bandung Sumedang KM. 21, Jatinagor Sumedang 45363
  • Cornelis Roos Algorithm Group, Delft University of Technology, Mekelweg 4, 2528 CD Delft




Optimization, uncertainty, conic, robust counterpart


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.


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