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For a robust optimization, that uncertainties of the reluctivities need to be included.
Therefore, in order to find a non-dominated globally solution a Pareto front technique need to be applied for a robust optimization.
The proposed approach is innovative, since stochastic modeling of uncertainties are combined with a topology optimization for minimal electromagnetic torque fluctuations (of the CT) and at the same time allowing for a robust optimization on the basis of a suitable optimization criterion.
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A robust optimization approach for optimal microgrid management considering wind power uncertainty is presented in [14], in which a time-series based autoregressive integrated moving average model is used to characterize the wind power uncertainty through interval forecasting.
A robust optimization formulation seeks for optimal solutions that optimize the objective function and meet the problem requirements for all possible uncertainties in constraint coefficients.
To deal with this problem, for the first time a robust optimization approach is applied to RAP with component mixing to produce a robust solution, which is relatively insensitive with respect to uncertainty in reliability of components.
Therefore, a robust optimization model is employed for optimizing the power dispatched from wind farms in this paper.
Averaging over multiple clusterings for multiple bootstrap resampled datasets provides a robust optimization result that is not dependent on a specific estimate of the optimal number of variable clusters.
Here, we formulate a robust optimization model for distribution and evacuation in the disaster response phase.
Leung and Wu (2004) proposed a robust optimization model for stochastic aggregate production planning.
Sakhaii et al. (2015) developed a robust optimization approach for a new integrated MILP model to solve a DCMS with unreliable machines and a production planning problem simultaneously.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com