This research proposes a novel approach for the robust optimization of the design of
hydrogen peroxide propulsion control systems using the efficient and advanced Teaching …

This study proposes a data-driven method for assessing reliability, based on the scarce
input dataset with multidimensional correlation. Since considering the distribution …

Dempster–Shafer evidence theory has been extensively applied in a variety of fields due to
its ability to solve knowledge reasoning and decision-making problem under uncertain …

This paper proposes three machine learning-based uncertainty set construction methods
and a novel uncertainty quantification method for robustness-based optimization. The …

Optimization equips engineers and scientists in a variety of fields with the ability to transcribe
their problems into a generic formulation and receive optimal solutions with relative ease …

In this paper, robust optimization methodologies for solving incomplete-information two-
person zero-sum and nonzero-sum games are developed that consider single or multiple …

Neural network (NN) based prediction or detection systems often perform excellently with
easy problems without considering 1-5% difficult problems. This work proposes an …

In this paper, we propose formulations and algorithms for robust portfolio optimization under
both aleatory uncertainty (ie, natural variability) and epistemic uncertainty (ie, imprecise …

The uncertainty information of design variables is included in the available representation
data, and there are differences among representation data from different sources. Therefore …

Many non-probabilistic approaches have been widely regarded as mathematical tools for
the representation of epistemic uncertainties. However, their heavy computational burden …