Distributionally robust optimization: A review
H Rahimian, S Mehrotra - arXiv preprint arXiv:1908.05659, 2019 - arxiv.org
The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …
have developed significantly over the last decade. Statistical learning community has also …
Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …
modern data lens, highlights key research challenges and promise of data-driven …
Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management
Renewable energy consumption in agriculture is ascending, catering to the food needs of
the rising population and protecting the environment. Maximizing renewable energy usage …
the rising population and protecting the environment. Maximizing renewable energy usage …
[HTML][HTML] Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era
Safe, efficient, and sustainable operations and control are primary objectives in industrial
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
Frameworks and results in distributionally robust optimization
H Rahimian, S Mehrotra - Open Journal of Mathematical Optimization, 2022 - numdam.org
The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …
have developed significantly over the last decade. The statistical learning community has …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods
This paper proposes a novel data-driven robust optimization framework that leverages the
power of machine learning and big data analytics for decision-making under uncertainty. By …
power of machine learning and big data analytics for decision-making under uncertainty. By …
Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization
For sustainable and reliable power systems operations integrating variable renewable
energy, it is essential to incorporate the uncertain intermittent power outputs. A novel robust …
energy, it is essential to incorporate the uncertain intermittent power outputs. A novel robust …
[HTML][HTML] New York State's 100% renewable electricity transition planning under uncertainty using a data-driven multistage adaptive robust optimization approach with …
Power system decarbonization is critical for combating climate change, and handling
systems uncertainties is essential for designing robust renewable transition pathways. In this …
systems uncertainties is essential for designing robust renewable transition pathways. In this …
Data-driven adaptive robust unit commitment under wind power uncertainty: A Bayesian nonparametric approach
This paper proposes a novel data-driven adaptive robust optimization (ARO) framework for
the unit commitment (UC) problem integrating wind power into smart grids. By leveraging a …
the unit commitment (UC) problem integrating wind power into smart grids. By leveraging a …