Sentence examples for using a robust optimization from inspiring English sources

Based on previous works, those forces are computed by using a robust optimization scheme.

Since some parameters are uncertain during the crisis, in order to let the model approach the reality, using a robust optimization approach, the model was developed in an uncertain condition.

Due to the stochastic nature of demand and cost parameters, the aforementioned model is developed to incorporate uncertainty using a robust optimization approach that can overcome the limitations of scenario-based solution methods, i.e., without excessive changes in complexity of the underlying base deterministic model.

The work in [33] used a robust optimization model for managing combined heat and power systems via linear decision rules.

This is reached through minimizing the finite element-experimental frequency relative deviations of the first eight short-circuit modes of the smart composite structure using a robust multi-objective evolutionary optimization procedure.

Therefore, in consideration of the aforementioned uncertainty and dynamic nature of humanitarian logistics, this study develops a dynamic optimization model using a robust stochastic approach.

The unknown transport and reaction parameters in the model are estimated simultaneously by minimizing the differences between available in vivo experimental data on muscle ischemia and corresponding model outputs in coupled with the resting linear flux balance constraints using a robust, nonlinear, constrained-based, reduced gradient optimization algorithm.

The problem is formulated using a stochastic robust optimization model in which uncertainties in the day-ahead market prices and in the driving requirements of electric vehicles are modeled using scenarios and confidence bounds, respectively.

To demonstrate this capability, a case study shows how a robust optimization scheme using genetic algorithms can improve product robustness to form error.

The methodology relies on a robust optimization technique using genetic algorithms, which is a research tool based on the principle of Darwinian evolution that is used to seek an optimal solution to a problem having a large number of adjustable parameters.

To further demonstrate the idea, we develop a robust optimization model using a non-linear von Neumann Morgenstern expected utility function and present two computational examples.