Handling Optimization under Uncertainty Problem Using Robust Counterpart Methodology
Abstract: 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.
Author: Diah Chaerani,
Cornelis Roos
Journal Code: jptindustrigg130012
