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
More filters
Affinely Adjustable Robust Location Transportation Problem
TL;DR: In this article, a robust location transportation problem with uncertain demand is considered, where the affine decision rules are employed to exploit the fact that while strategic decisions such as the location and capacity of the facilities need to be immediately implemented, operational decisions, such as final production and distribution of goods can be delayed until the actual demand is observed.
Energy and Reserve Management in Interconnected Systems including Electric Railway and Public Power Grids: Operation, Market Strategies and Capacity Expansion
TL;DR: In this article, the problem of joint energy and reserve scheduling in an ERPS has been addressed, and two different approaches, adaptive robust optimization and stochastic optimization, have been proposed for dealing with uncertainties in this scheduling problem.
Journal ArticleDOI
Robust recycling facility location with clustering
Tianqi Liu,Guiyu Li +1 more
TL;DR: A structured paradigm of finitely adaptive distributionally robust optimization, which is developed with a learning machinery integrating clustering analysis and χ 2 -divergence-based distributional ambiguity set is utilized to tackle the ambiguity and unobservability of feedstock condition.
Posted Content
Oracle-Based Algorithms for Binary Two-Stage Robust Optimization
Nicolas Kämmerling,Jannis Kurtz +1 more
TL;DR: This work presents an algorithm to calculate efficiently lower bounds for the binary two-stage robust problem by solving alternately the underlying deterministic problem and an adversarial problem and shows that the latter lower bound can be implemented in a branch and bound procedure.
Posted Content
Multi-stage Robust Transmission Expansion Planning under Socioeconomic and Environmental Changes
TL;DR: This paper drops the classical stationarity assumption and extends an existing adaptive robust transmission expansion planning formulation for non-stationary situations and provides information not only about what additional lines must be installed but the construction timing during the study horizon as well.
References
More filters
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.