Add to ORKGWe review and develop different tractable approximations to individual chance-constrained problems in robust optimization on a variety of uncertainty sets and show their interesting connections with bounds on the conditional-value-at-risk (CVaR) measure. We extend the idea to joint chance-constrained problems and provide a new formulation that improves upon the standard approach. Our approach builds on a classical worst-case bound for order statistics problems and is applicable even if the constraints are correlated. We provide an application of the model on a network resource allocation problem with uncertain demand
The objective of this thesis has been the study of risk analysis and optimization under uncertainty....
Optimization problems face random constraint violations when uncertainty arises in constraint parame...
It is unrealistic to formulate the problems arising under uncertain environments as deterministic op...
We review and develop different tractable approximations to individual chance constrained problems i...
In this paper we review the different tractable approximations of individual chance constraint probl...
We study stochastic optimization problems with chance and risk constraints, where in the latter, ris...
We consider a joint-chance constraint (JCC) as a union of sets, and approximate this union using bou...
In optimization problems appearing in fields such as economics, finance, or engineering, it is often...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
Abstract This paper investigates the computational aspects of distributionally ro-bust chance constr...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
Chance constrained problems are optimization problems where one or more constraints ensure that the ...
In optimization problems appearing in fields such as economics, finance, or engineering, it is often...
We study joint chance constraints where the distribution of the uncertain parameters is only known t...
When designing or upgrading a communication network, operators are faced with a major issue, as unce...
The objective of this thesis has been the study of risk analysis and optimization under uncertainty....
Optimization problems face random constraint violations when uncertainty arises in constraint parame...
It is unrealistic to formulate the problems arising under uncertain environments as deterministic op...
We review and develop different tractable approximations to individual chance constrained problems i...
In this paper we review the different tractable approximations of individual chance constraint probl...
We study stochastic optimization problems with chance and risk constraints, where in the latter, ris...
We consider a joint-chance constraint (JCC) as a union of sets, and approximate this union using bou...
In optimization problems appearing in fields such as economics, finance, or engineering, it is often...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
Abstract This paper investigates the computational aspects of distributionally ro-bust chance constr...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
Chance constrained problems are optimization problems where one or more constraints ensure that the ...
In optimization problems appearing in fields such as economics, finance, or engineering, it is often...
We study joint chance constraints where the distribution of the uncertain parameters is only known t...
When designing or upgrading a communication network, operators are faced with a major issue, as unce...
The objective of this thesis has been the study of risk analysis and optimization under uncertainty....
Optimization problems face random constraint violations when uncertainty arises in constraint parame...
It is unrealistic to formulate the problems arising under uncertain environments as deterministic op...