One of the strengths of our approach is the ability to retrofit existing robust optimization models by replacing their uncertainty sets with our proposed sets, thereby creating new data-driven models that satisf
In Conditional Value-at-Risk robust optimization, we try to find an optimal decision that performs well in a subset of some worst-case estimators of the random parameters of the optimization problem. With functional robust optimization, we construct a decision-making problem using the worst ...
Liu, Chen, Lisser, and Xu (2019) also construct a robust mean-conditional value at risk portfolio selection model where the uncertainty set is developed under the framework of the technique proposed by Delage and Ye (2010). Other than moment-based uncertainty sets that relies on data-driven ...
A data driven robust optimization is proposed in Ref. [30] to model the near-zero carbon emission power plant in the presence of power-to-gas and the carbon capture, facilities. The obtained results represent that proposed robust framework can resist the interference of wind power output ...
On the Home tab go to the Style group and click the arrow next to Conditional Formatting. Opt for Highlight Cell Rules, Duplicate Values, and input the values to identify duplicates, highlighting them accordingly. 91. In Excel, explain the difference Between COUNT, COUNTA, COUNTBLANK, and ...
This poses challenges to the physics-based modeling of silks, and as such makes data-driven methods that are agnostic to structural data advantageous for this purpose. This calls for a representation that can capture the effect of each spidroin on the properties of the spider silk. With the ...
(i.e., for those edges that are present in the CN graph). On the other hand, the probability distribution function for a BN is represented by a factorization of conditional multivariate Gaussian probabilities and the parameters are the linear regression coefficients of a variable on its ...
Data-driven Science, Modeling and Theory Building Data Engineering Data publication and archiving Machine Learning Data acquisition 1Introduction Throughout the modern history of humankind, manufacturing has been of central importance to economic advancement. According to statistics from the World Bank, the...
[33] proposed a robust DEA model where the inputs/outputs are modeled with a probability distribution, and robust efficiency scores are distinguished into an unconditional expected score, conditional expected score, and the maximum entropy expected score. Most recently, Xiao et al. [67] integrated...
In particular, it is best-suited for the optimization problem over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations [187]. GP is a data-driven approach, also known as a kernel-based learning algorithm. GP have been demonstrated to be ...