Optimization problems due to noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this paper, we introduce a framework that considers the uncertain dat...

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...

the basic models of data envelopment analysis (dea) are designed in such a way that the values of input and output indicators should be identified and known in them. in other words, these models are not used to consider inaccurate, interval, fuzzy, judgment data. in this paper, the aim is to not only review the past researches about the efficiency of the units by interval data and represent the...

Robust optimization problems, which have uncertain data, are considered. We prove surrogate duality theorems for robust quasiconvex optimization problems and surrogate min-max duality theorems for robust convex optimization problems. We give necessary and sufficient constraint qualifications for surrogate duality and surrogate min-max duality, and show some examples at which such duality result...

Optimization of reservoir parameters is an important issue in petroleum exploration and production. The Ant Colony Optimization(ACO) is a recent approach to solve discrete and continuous optimization problems. In this paper, the Ant Colony Optimization is usedas an intelligent tool to estimate reservoir rock properties. The methodology is illustrated by using a case study on shear wave velocity...