none3siWe improve the well-known result presented in Bertsimas and Sim (Math Program B98:49-71, 2003) regarding the computation of optimal solutions of Robust Combinatorial Optimization problems with interval uncertainty in the objective function coefficients. We also extend this improvement to a more general class of Combinatorial Optimization problems with interval uncertainty.noneEduardo Álvarez-Miranda;Ivana Ljubić;Paolo TothEduardo Álvarez-Miranda;Ivana Ljubić;Paolo Tot
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
Uncertainty in optimization is not a new ingredient. Diverse models considering uncertainty have bee...
In optimization, it is used to deal with uncertain and inaccurate factors which make difficult the a...
Robust combinatorial optimization problems with cardinality constrained uncertainty may be solved by...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
In this thesis, we study robust combinatorial problems with interval data. We introduce several new ...
We provide test instances for robust combinatorial optimization with budget uncertainty in the objec...
Robust optimization (RO) has become a central framework to handle the uncertainty that arises in the...
AbstractWe consider combinatorial optimization problems with uncertain parameters of the objective f...
Based on the recent approach of Bertsimas and Sim (2004, 2003) to robust optimization in the presenc...
We consider discrete optimization problems with interval uncertatinty of objective function coeffici...
We extend the standard concept of robust optimization by the introduction of an alternative solution...
International audienceWe present in this paper a new model for robust combinatorial optimization wit...
The traveling salesman problem is one of the most famous combinatorial optimization problems, and ha...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
Uncertainty in optimization is not a new ingredient. Diverse models considering uncertainty have bee...
In optimization, it is used to deal with uncertain and inaccurate factors which make difficult the a...
Robust combinatorial optimization problems with cardinality constrained uncertainty may be solved by...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
In this thesis, we study robust combinatorial problems with interval data. We introduce several new ...
We provide test instances for robust combinatorial optimization with budget uncertainty in the objec...
Robust optimization (RO) has become a central framework to handle the uncertainty that arises in the...
AbstractWe consider combinatorial optimization problems with uncertain parameters of the objective f...
Based on the recent approach of Bertsimas and Sim (2004, 2003) to robust optimization in the presenc...
We consider discrete optimization problems with interval uncertatinty of objective function coeffici...
We extend the standard concept of robust optimization by the introduction of an alternative solution...
International audienceWe present in this paper a new model for robust combinatorial optimization wit...
The traveling salesman problem is one of the most famous combinatorial optimization problems, and ha...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
Uncertainty in optimization is not a new ingredient. Diverse models considering uncertainty have bee...
In optimization, it is used to deal with uncertain and inaccurate factors which make difficult the a...