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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Journal ArticleDOI
Two-Stage Robust Optimization Under Decision Dependent Uncertainty
TL;DR: In this article , a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions is introduced, and a novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision coupling.
Book ChapterDOI
Multi-Stage Adaptive Robust Optimization over Bioconversion Product and Process Networks with Uncertain Feedstock Price and Biofuel Demand
TL;DR: In this paper, a multi-stage adaptive robust optimization approach is proposed to handle feedstock price and product demand uncertainty in order to find the minimum total annualized cost of a bioconversion product and process network.
Models and methods for electricity and gas markets in a low-carbon economy
TL;DR: In this paper, two planning models to efficiently integrate renewable and storage units in electric energy system are proposed, and a particular emphasis is posed on both the possible re-negotiation of the long-term natural gas contracts and on the security of supply of the Italian gas market.
Journal ArticleDOI
Asymmetric Information in Military Microgrid Confrontations—Evaluation Metric and Influence Analysis
TL;DR: Results on various levels of attack strength validated the effectiveness and significance of asymmetric information in eliminating the attack damage and improving the defensive performance.
A practicable robust counterpart formulation for decomposable functions: A network congestion case study
TL;DR: In this paper, the authors propose a new robust optimization formulation that circumventes the computational burden in problems where the cost decomposes as the sum of costs that each involve a single decision variable, by exploiting the fact that in this formulation the worst-case cost function can be expressed as a convex combination between a nominal and an upper bounding cost function.
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.
Journal ArticleDOI
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.