The present paper addresses the class of two-stage robust optimization problems which can be formulated as mathematical programs with uncertainty on the right-hand side coefficients (RHS uncertainty). The wide variety of applications and the fact that many problems in the class have been shown to be NP-hard, motivates the search for efficiently solvable special cases. Accordingly, the first objective of the paper is to provide an overview of the most important applications and of various polynomial or pseudo-polynomial special cases identified so far. The second objective is to introduce a new subclass of polynomially solvable robust optimization problems with RHS uncerta...
Multi-stage linear optimization is an integral modeling paradigm in supply chain, energy planning, a...
We consider optimization problems where the exact value of the input data is not known in advance an...
We propose an approach to two-stage linear optimization with recourse that does not in-volve a proba...
The present paper addresses the class of two-stage robust optimization problems which can ...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
Abstract In this paper, we propose a new methodology for handling opti-mization problems with uncert...
The ever growing performances of mathematical programming solvers allows to be thinking of solving m...
Most optimization problems in real life do not have accurate estimates of the prob-lem parameters at...
Robust optimization has become an important paradigm to deal with optimization under uncertainty. Ad...
In this work we consider uncertain optimization problems where no probability distribution is known....
This paper describes models and solution algorithms for solving robust multistage decision problems ...
In robust optimization, the general aim is to find a solution that performs well over a set of possi...
International audienceThe various robust linear programming models investigated so far in the litera...
Abstract Uncertainty is often present in environmental and energy economics. Tra-ditional approaches...
This dissertation is a collection of previously-published manuscript and conference papers. In this ...
Multi-stage linear optimization is an integral modeling paradigm in supply chain, energy planning, a...
We consider optimization problems where the exact value of the input data is not known in advance an...
We propose an approach to two-stage linear optimization with recourse that does not in-volve a proba...
The present paper addresses the class of two-stage robust optimization problems which can ...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
Abstract In this paper, we propose a new methodology for handling opti-mization problems with uncert...
The ever growing performances of mathematical programming solvers allows to be thinking of solving m...
Most optimization problems in real life do not have accurate estimates of the prob-lem parameters at...
Robust optimization has become an important paradigm to deal with optimization under uncertainty. Ad...
In this work we consider uncertain optimization problems where no probability distribution is known....
This paper describes models and solution algorithms for solving robust multistage decision problems ...
In robust optimization, the general aim is to find a solution that performs well over a set of possi...
International audienceThe various robust linear programming models investigated so far in the litera...
Abstract Uncertainty is often present in environmental and energy economics. Tra-ditional approaches...
This dissertation is a collection of previously-published manuscript and conference papers. In this ...
Multi-stage linear optimization is an integral modeling paradigm in supply chain, energy planning, a...
We consider optimization problems where the exact value of the input data is not known in advance an...
We propose an approach to two-stage linear optimization with recourse that does not in-volve a proba...