Handling Optimization under Uncertainty Problem Using Robust Counterpart Methodology
| Main Authors: | Chaerani, Diah; Faculty of Mathematics and Natural Sciences, Department of Mathematik, Universitas Padjadjaran, Jl. Raya Bandung Sumedang KM. 21, Jatinagor Sumedang 45363, Roos, Cornelis; Algorithm Group, Delft University of Technology, Mekelweg 4, 2528 CD Delft |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Institute of Research and Community Outreach - Petra Christian University
, 2013
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| Subjects: | |
| Online Access: |
http://jurnalindustri.petra.ac.id/index.php/ind/article/view/18848 http://jurnalindustri.petra.ac.id/index.php/ind/article/view/18848/18547 |
Daftar Isi:
- 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.