In ‎the ‎portfolio ‎optimization, ‎the ‎goal ‎is ‎to ‎distribute ‎the ‎ fixed capital ‎on a‎ ‎set ‎of‎investment ‎opportunities ‎to ‎maximize ‎return ‎while ‎managing ‎risk. ‎Risk ‎and ‎return ‎are ‎quantiti es ‎that ‎are ‎used ‎as ‎input ‎paramete‎rs ‎for ‎the ‎optimal ‎allocation ‎of ‎the ‎capital ‎in ‎the ‎suggested ‎models. ‎ But ‎these ‎quantities ‎are ‎not ‎known ‎at ‎the ‎time ‎of ‎the ‎...

Sauleh Siddiqui*, Steven A Gabriel and Shapour Azarm Johns Hopkins University, Baltimore, MD, USA; Department of Civil Engineering, Applied Mathematics & Statistics, and Scientific Computation Program, University of Maryland, College Park, MD, USA; and Department of Mechanical Engineering, Applied Mathematics & Statistics, and Scientific Computation Program, University of Maryland, College Park...

We study the strategic design of a supply chain for new products. Planning a supply chain for a new product requires addressing both uncertainties in demand and cost and changes in market conditions over time. We develop a mathematical programming model for new products that includes both financial viability and robustness relative to uncertainty. An example based on a Fortune 200 company’s eff...

In this paper, we propose a new methodology for handling optimization problems with uncertain data. With the usual Robust Optimization paradigm, one looks for the decisions ensuring a required performance for all realizations of the data from a given bounded uncertainty set, whereas with the proposed approach, we require also a controlled deterioration in performance when the data is outside th...

Sample average approximation (SAA) is a widely popular approach to data-driven decisionmaking under uncertainty. Under mild assumptions, SAA is both tractable and enjoys strong asymptotic performance guarantees. Similar guarantees, however, do not typically hold in finite samples. In this paper, we propose a modification of SAA, which we term Robust SAA, which retains SAA’s tractability and asy...

This paper proposes scalable and fast algorithms for solving the Robust PCA problem, namely recovering a low-rank matrix with an unknown fraction of its entries being arbitrarily corrupted. This problem arises in many applications, such as image processing, web data ranking, and bioinformatic data analysis. It was recently shown that under surprisingly broad conditions, the Robust PCA problem c...

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems so far. In the present study, a simple optimization (SOPT) algorithm with two main steps namely exploration and exploitation, is provided for practical applications. Aside from a reasonable rate of convergence attained, the ease in its implementation and dependen...

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...

This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class of convex polynomial optimization problems in the face of data uncertainty. The class of convex optimization problems, called robust SOS-convex polynomial optimization problems, includes robust quadratically constrained convex optimization problems and robust separable convex polynomial optimiza...

Resource tradeoffs can often be established by solving an appropriate robust optimization problem for a variety of scenarios involving constraints on optimization variables and uncertainties. Using an approach based on sequential convex programming, we demonstrate that quantum gate transformations can be made substantially robust against uncertainties while simultaneously using limited resource...