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<lb>In this paper, we focus on a linear optimization problem with uncertainties, having expectations<lb>in the objective and in the set of constraints. We present a modular framework to obtain an approx-<lb>imate solution to the problem that is distributionally robust, and more flexible than the standard<lb>technique of using linear rules. Our framework(More)
We introduce an algebraic modeling language, named ROME, for a class of robust optimization<lb>problems. ROME serves as an intermediate layer between the modeler and optimization solver<lb>engines, allowing modelers to express robust optimization problems in a mathematically meaningful<lb>way. In this paper, we highlight key features of ROME which(More)
We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a so-called Partitioned Value-atRisk (PVaR) measure by using half-space statistical information. Using simulated and real data, the PVaR approach generates better risk-return tradeoffs in the optimal(More)
The existence of important socioeconomic disparities in health and mortality is a well-established fact. Many pathways have been adduced to explain inequality in life spans. In this article we examine one factor that has been somewhat neglected: People with different levels of education get sorted into jobs with different degrees of exposure to workplace(More)
We investigate the use of a Genetic Algorithm (GA) to design a set of photonic crystals (PCs) in one and two dimensions. Our flexible design methodology allows us to optimize PC structures for specific objectives. In this paper, we report the results of several such GA-based PC optimizations. We show that the GA performs well even in very complex design(More)
We describe robust optimization procedures for controlling total completion time penalty plus crashing cost in projects with uncertain activity times. These activity times arise from an unspecified distribution within a family of distributions with known support, mean and covariance. We develop linear and linear-based decision rules for making decisions(More)