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
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Increasing Supply Chain Robustness through Process Flexibility and Inventory
TL;DR: In this article, a hybrid strategy that uses both process flexibility and inventory for supply chain risk mitigation is studied, where the interplay between process flexibility with inventory is modeled as a two-stage robust optimization problem; in the first stage, the firm allocates inventory, and in the second stage after uncertainties are realized, after schedules its flexible production to minimizes its cost.
Journal ArticleDOI
Charging Strategies of EV Aggregator Under Renewable Generation and Congestion: A Normalized Nash Equilibrium Approach
Wei Wei,Feng Liu,Shengwei Mei +2 more
TL;DR: A normalized Nash equilibrium (NNE) is used to characterize a fair charging decision among the participants and it is shown that when the commission fee charged by the aggregator is a convex function and proportionally shared by the consumers, the NNE can be efficiently computed from a particular convex optimization problem, and is noninferior if the commission Fee function is separable in players.
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Distribution Network Electric Vehicle Hosting Capacity Maximization: A Chargeable Region Optimization Model
TL;DR: In this article, the authors proposed the concept of an EV chargeable region to evaluate the distribution network (DN) EV hosting capacity, i.e., how much EV charging demand can be accommodated in a DN, within which the technical constraints of DN (e.g., voltage deviation) are guaranteed and EV owners' charging requests are maximally ensured.
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Duality in Two-Stage Adaptive Linear Optimization: Faster Computation and Stronger Bounds
TL;DR: It is shown that the optimal primal affine policy can be directly obtained from the optimal affine Policy in the dual formulation, and an explanation and associated empirical evidence that offer insight on which characteristics of the dualized formulation make computations faster are provided.
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On the relation between flexibility analysis and robust optimization for linear systems
TL;DR: In this article, a comparison between flexibility analysis and robust optimization from a historical perspective is presented, and new formulations for the three classical flexibility analysis problems (flexibility test, flexibility index, and design under uncertainty) based on duality theory and affinely adjustable robust optimization (AARO) approach are proposed.
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.
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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.