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
Journal ArticleDOI
Robust optimal shunt dispatch method in wind farm integration area
Journal ArticleDOI
Towards leveraging discrete grid flexibility in chance-constrained power system operation planning
TL;DR: This paper uses a scenario-based approach to determine a planning decision and relies on theoretical results to compute an upper bound on the probability of being able to meet the N-1 criterion in operation and estimates the actual value of this probability through Monte Carlo simulation.
Book ChapterDOI
Solving Large Scale Optimization Problems in the Transportation Industry and Beyond Through Column Generation
TL;DR: This chapter will discuss monthly airline crew schedule optimization for bidlines, crew pairing optimization, and integrated modeling of fleet and routing in the optimization of aircraft scheduling, as well as progress in the large scale optimization space made possible by technologies such as parallel processing, big data, and better chips.
Journal ArticleDOI
An inexact column-and-constraint generation method to solve two-stage robust optimization problems
TL;DR: In this article , an inexact column-and-constraint generation (i-C&CG) method is proposed to solve two-stage robust optimization problems, which allows solutions to the master problems to be inexact, which is desirable when solving large scale and/or challenging problems.
Proceedings ArticleDOI
Leveraging big data for adaptive robust optimization of scheduling under uncertainty
Chao Ning,Fengqi You +1 more
TL;DR: A Bayesian nonparametric model - the Dirichlet process mixture model - is adopted to extract the information embedded within uncertainty data via a variational inference algorithm and is seamlessly integrated with adaptive optimization approach through a novel four-level robust optimization framework.
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.