the development of optimization and mathematical models for closed loop supply chain (clsc) design has attracted considerable interest over the past decades. however, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. the aim of this paper, therefore, is to propose a n...

• First treatment of standard quadratic optimization problems under uncertainty with two-stage stochastic approach. Efficient and tractable upper lower bounds for this NP-hard problem. Modern first-order method (pairwise Frank-Wolfe) enhanced copositive techniques aim at scalability bounds. Two-stage decision are characterized by the property that in stage one part has to be made before relevan...

In “Two-Stage Sample Robust Optimization,” Bertsimas, Shtern, and Sturt investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the type-infinity Wasserstein ambiguity set. Their main result establishes that this scheme is asymptotically optimal stochastic problems; is, under mild...

In the field of data-driven optimization under uncertainty, scenario reduction is a commonly used technique for computing smaller number scenarios to improve computational tractability and interpretability. However traditional approaches do not consider decision quality when these scenarios. “Optimization-Based Scenario Reduction Data-Driven Two-Stage Stochastic Optimization,” Bertsimas Mundru ...

A two-stage PID algorithm is proposed with focus on fulfilling some important general requirements such as settling time, overshoot, size of control signal, disturbance rejection and robustness. The proposed method has two main goals. The first is to create an automatic PID computing algorithm giving decent results for a great variety of systems with different design requirements and different ...

We propose a two-stage optimization approach for protein folding simulation in the FCC lattice, inspired from the phenomenon of hydrophobic collapse. Given a protein sequence, the first stage of the approach produces compact protein structures with the maximal number of contacts among hydrophobic monomers, using the CPSP tools for optimal structure prediction in the HP model. The second stage u...