Integrated performance and risk management is the key lever to increase shareholder value holistically. In this paper, we develop a corresponding framework for value-based performance and risk optimization in supply chains. Economic Value Added (EVA) as a prevalent metric of value-based performance is applied to mid-term sales and operations planning (S&OP). Robust optimization methods are util...

Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides a broad and unifying view on much of the prior work...

A dynamic phasor model for lowand medium-voltage distribution grids is presented. This model accurately represents the transient dynamics of this type of grid, and its low order makes it well suited for control design. A novel fixed-structure, robust control design method based on convex optimization is then used to design a decentralized low-order controller that shows a significantly improved...

In this paper, we propose a framework for robust optimization that relaxes the standard notion of robustness by allowing the decision-maker to vary the protection level in a smooth way across the uncertainty set. We apply our approach to the problem of maximizing the expected value of a payoff function when the underlying distribution is ambiguous and therefore robustness is relevant. Our prima...

We provide an overview of our main results about studying Linear Programming Problems whose coefficient matrix is subject to uncertainty and the uncertainty is modeled through a multi-band set. Such an uncertainty set generalizes the classical one proposed by Bertsimas and Sim [3] and is particularly suitable in the common case of arbitrary non-symmetric distributions of the parameters. Our inv...

In this paper we address the problem of predicting human body configurations and appearance for the task of humanmotion tracking in video. Unlike many previous approaches that relied on simplified body models, we are able to use detailed models of body shape. Having rendered the body model onto the current video frame, we make use of the optical flow information to propagate the body configurat...

Optimization models for the production scheduling of power-intensive processes such as air separation can help realizing significant economical savings. However, if the electricity is procured in the dayahead or real-time market, the forecast for the hourly electricity prices contains a significant amount of uncertainty. In this work, we apply robust optimization to the uncertain electricity pr...

During the tactical planning of multi-period vehicle routing operations, dispatchers must incorporate enough flexibility in their routing plans to accommodate potential customers who have not yet called in to place their order. We develop an Adaptive Robust Optimization framework to address this challenging problem and solve instances to guaranteed optimality.

We study the minimum cut problem in the presence of uncertainty and show how to apply a novel robust optimization approach, which aims to exploit the similarity in subsequent graph measurements or similar graph instances, without posing any assumptions on the way they have been obtained. With experiments we show that the approach works well when compared to other approaches that are also oblivi...