The robust optimization methodology that we have introduced so far is built on a fundamental modeling approach, based set-theoretic, deterministic uncertainty models.

In this paper we develop tight bounds on the expected values of several risk measures that are of interest to us. This work is motivated by the robust optimization models arising from portfolio selection problems. The basic setting is to find a portfolio which maximizes (respectively minimizes) the expected utility (respectively disutility) values, in the midst of infinitely many possible ambig...

The paper introduces the worst-case portfolio optimization models within robust framework for maximizing return through either mean or median metrics. risk in is quantified by Gini difference. We put forward under mixed and interval+polyhedral uncertainty sets. proposed turn out to be linear integer programs set, semidefinite program set. performance comparison of on listed stocks Euro Stoxx 50...

This article aims to investigate the location of dry ports by providing a two-stage GIS-optimization model. At the first stage, the appropriate points for establishing dry ports were identified using GIS and hierarchical analysis process; then, the suitable points were introduced as the potential points to the second stage model. At the second stage, by providing a multi-objective integer model...

In this work, we consider the problem of scheduling under uncertainty where the uncertain problem parameters can be described by a known probability distribution function. A novel robust optimization methodology, originally proposed by Lin, Janak, and Floudas [Lin, X., Janak, S. L., & Floudas, C. A. (2004). A new robust optimization approach for scheduling under uncertainty: I. Bounded uncertai...

In this paper, single stage stochastic programs with ambiguous distributions for the involved random variables are considered. Though the true distribution is unknown, existence of a reference measure P̂ enables the construction of non-parametric ambiguity sets as Kantorovich balls around P̂ . The resulting robustified problems are infinite optimization problems and can therefore not be solved co...

In nonlinear robust identification context, a process model is represented by a nominal model and possible deviations. With parametric models this process model can be expressed as the so-called Feasible Parameter Set (FPS), which derives from the minimization of identification error specific norms. In this work, several norms are used simultaneously to obtain the FPS. This fact improves the mo...

in this paper a new strategy is proposed to design a fixed-structure robust controller for a flexible beam.  robust controller designed by the conventional  loop­ shaping method is not appropriate for a beam because of its high order and complicated form. fixed-structure  loop shaping control in conjunction with particle swarm optimization (pso)algorithm  is used to overcome this drawback. the ...

not available at time of publication. Dimitris Bertsimas Massachusetts Insitute of Technology [email protected] MS8 Less is More: Robustness and Sparsity in Multivariate Statistics We describe applications of robust optimization in the context of machine learning, with a focus on classification and regression, principal component analysis and fitting of graphical models. In these tasks, sparsity...