... accounts for both the uncertainty associated with design inputs and the uncertainty of performance predictions from other disciplinary simulation tools. These implicit uncertainty estimates are used as the basis for a new robust collaborative optimization (RCO) framework . The bilevel robust optimization...

... of uncertainties, robust optimization (RO) algorithms have been developed to obtain solutions being not only optimal but also less sensitive to uncertainties. Based on how parameter uncertainty is modeled, there are two categories of RO approaches: interval-based and probability-based. In real-world engineering...

... data from the petrochemical industry. Supply chain network design robust optimization piecewise linearization 1 Copyright © 2013 by ASME Robust Supply Chain Network Design by Considering Demand-side Uncertainty and Supply-side Disruption Shabnam Rezapour The Systems Realization Laboratory...

... discipline, mixed continuous-discrete variables, and when there is a need to account for uncertainty and also uncertainty propagation across disciplines. We present a Multiobjective collaborative Robust Optimization (McRO) approach for this class of problems that have interval uncertainty in their parameters...

... We present a new robust optimization method that ensures feasibility of an optimized design when there are uncontrollable variations in design parameters. This method is developed based on the notion of a sensitivity region, which is a measure of how far a feasible design is from the boundary...

... whose properties can be used to predict that design’s sensitivity. Our method estimates such a region using a worst-case scenario analysis and uses that estimate in a bi-level robust optimization approach. We present a numerical and an engineering example to demonstrate the applications of our method...