Journal ArticleDOI
Solving two-stage robust optimization problems using a column-and-constraint generation method
TLDR
A computational study on a two-stage robust location-transportation problem shows that the column-and-constraint generation algorithm performs an order of magnitude faster than existing Benders-style cutting plane methods.About:
This article is published in Operations Research Letters.The article was published on 2013-09-01. It has received 1010 citations till now. The article focuses on the topics: Robust optimization & Cutting-plane method.read more
Citations
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A data-driven distributionally robust approach for the optimal coupling of interdependent critical infrastructures under random failures
TL;DR: In this paper , the authors proposed a data-driven distributionally robust approach for the optimal coupling of interdependent critical infrastructures such as energy systems, transportation networks and telecommunications networks.
Journal ArticleDOI
Robust dispatching of integrated energy system considering economic operation domain and low carbon emission
TL;DR: In this article , the authors proposed a robust optimization algorithm based on economic operation domain, which can reduce the total cost and the economy can be improved by adjusting the scheduling scheme according to the new energy output under the premise of ensuring the time coupling constraint.
Journal ArticleDOI
A two-stage robust optimization model for a virtual power plant considering responsiveness-based electric vehicle aggregation
TL;DR: Considering the uncertainty of wind power (WP), photovoltaic power (PV), and load, a two-stage robust optimization model for virtual power plant (VPP) is proposed, with a focus on calculating the available capacity of electric vehicle (EV) aggregated virtual energy storage (VES) as discussed by the authors .
Journal ArticleDOI
Adaptive Robust Ac Optimal Power Flow Considering Intrahour Uncertainties
B. Akbari,Giovanni Sansavini +1 more
A mixed-integer approximation of robust optimization problems with mixed-integer adjustments
TL;DR: In this article , a mixed-integer approximation of adjustable-robust optimization (ARO) problems is proposed, where continuous and discrete variables on the lowest level are assumed to be independent.
References
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Journal ArticleDOI
The Price of Robustness
Dimitris Bertsimas,Melvyn Sim +1 more
TL;DR: In this paper, the authors propose an approach that attempts to make this trade-off more attractive by flexibly adjusting the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations.
The price of the robustness
D Bertsimas,M Sim +1 more
TL;DR: An approach is proposed that flexibly adjust the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations, and an attractive aspect of this method is that the new robust formulation is also a linear optimization problem, so it naturally extend to discrete optimization problems in a tractable way.
Journal ArticleDOI
Robust Convex Optimization
Aharon Ben-Tal,Arkadi Nemirovski +1 more
TL;DR: If U is an ellipsoidal uncertainty set, then for some of the most important generic convex optimization problems (linear programming, quadratically constrained programming, semidefinite programming and others) the corresponding robust convex program is either exactly, or approximately, a tractable problem which lends itself to efficientalgorithms such as polynomial time interior point methods.
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Generalized Benders decomposition
TL;DR: In this paper, the extremal value of the linear program as a function of the parameterizing vector and the set of values of the parametric vector for which the program is feasible were derived using linear programming duality theory.