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Probabilistic optimization techniques in smart power system
Uncertainties are the most significant challenges in the smart power system, necessitating
the use of precise techniques to deal with them properly. Such problems could be effectively …
the use of precise techniques to deal with them properly. Such problems could be effectively …
Process optimization of microbial fermentation with parameter uncertainties via distributionally robust discrete control
J Wang, C Chen, F Zhao, J Wang, A Li - Journal of Process Control, 2023 - Elsevier
There are some uncertain kinetic parameters in microbial fermentation system because of
the unclear intracellular metabolic mechanisms. Considering the affection of these uncertain …
the unclear intracellular metabolic mechanisms. Considering the affection of these uncertain …
A data-driven robust optimization algorithm for black-box cases: An application to hyper-parameter optimization of machine learning algorithms
The huge availability of data in the last decade has raised the opportunity for the better use
of data in decision-making processes. The idea of using the existing data to achieve a more …
of data in decision-making processes. The idea of using the existing data to achieve a more …
Multi-stage distributionally robust convex stochastic optimization with Bayesian-type ambiguity sets
The existent methods for constructing ambiguity sets in distributionally robust optimization
often suffer from over-conservativeness and inefficient utilization of available data. To …
often suffer from over-conservativeness and inefficient utilization of available data. To …
Globalized distributionally robust optimization based on samples
Y Li, W ** rapidly, and
the Vehicle Routing Problem (VRP) has been widely concerned. In this paper, we focus on …
the Vehicle Routing Problem (VRP) has been widely concerned. In this paper, we focus on …
Data-driven distributionally robust optimization via optimal transport with order cone constraints
A Esteban-Pérez, JM Morales - arxiv, 2019 - dml.mathdoc.fr
We tackle stochastic programs affected by ambiguity about the probability law that governs
their uncertain parameters. Using Optimal Transport Theory, we construct an ambiguity set …
their uncertain parameters. Using Optimal Transport Theory, we construct an ambiguity set …
Theory and applications of Distributionally Robust Optimization with side data
A Esteban-Pérez - 2022 - riuma.uma.es
Nowadays, a large amount of varied data is being generated which, when made available to
the decision maker, constitutes a valuable resource in optimization problems. These data …
the decision maker, constitutes a valuable resource in optimization problems. These data …
[CITATION][C] Data-driven Robust Optimization: References and Keywords
M Namakshenas