Uncertain parameters appear in many optimization problems raised by real-world applications. To handle such problems, several approaches to model uncertainty are available, such as stochastic programming and robust optimization. This study is focused on robust optimization, in particular, the criteria to select and determine a robust solution. We provide an overview on robust optimization criteria and introduce two new classi cations criteria for measuring the robustness of both scenarios and solutions. They can be used independently or coupled with classical robust optimization criteria and could work as a complementary tool for intensification in local searches.
Citation
A. A. COCO, E.L. SOLANO-CHARRIS, A. C. SANTOS, C. PRINS, T. F. NORONHA. Robust optimization criteria: state-of-the-art and new issues, Technical report UTT-LOSI-14001, ISSN :2266-5064, Université de Technologie de Troyes, Troyes, France, June, 2014.