this paper proposes a family of robust counterpart for uncertain linear programs (lp) which is obtained for a general definition of the uncertainty region. the relationship between uncertainty sets using norm bod-ies and their corresponding robust counterparts defined by dual norms is presented. those properties lead us to characterize primal and dual robust counterparts. the researchers show t...

p2x 7 antagonist activity for a set of 49 molecules of the p2x 7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. the activity of these compounds was estimated by means of combination of principal component analysis (pca), as a well-known data reduction method, genetic algorithm (ga), as a variable selection technique, ...

Our goal in this talk is to present unifying concepts for both stochastic and robust optimization problems involving infinite uncertainty sets. We apply methods from vector optimization in general spaces, set-valued optimization and scalarization techniques to develop a unified characterization of different concepts of robust optimization and stochastic programming. These methods provide new in...

Robust optimisation is a well established concept to deal with uncertainty. In particular, recovery-robust models are suitable for real-world contexts, where a certain amount of recovery – although limited – is often available. In this paper we describe a general framework to optimise event-based problems against delay propagation. We also present a real-world application to train platforming i...

In this paper we survey the primary research, both theoretical and applied, in the field of Robust Optimization (RO). Our focus will be on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying the most prominent theoretical results of RO over the past decade, we will also present some recent results ...