This paper explores the idea that two-stage worst-case regret minimization problems with either objective or right-hand side uncertainty can be reformulated ...

We consider a class of min-max robust problems in which the functions that need to be robustified can be decomposed as the sum of arbitrary functions. This...

Drawing on statistical learning theory, we derive out-of-sample and optimality guarantees about the investment strategy obtained from a regularized portfoli...

This paper considers a dynamic Emergency Medical Services (EMS) network design problem and introduces two novel two-stage stochastic programming formulatio...

Randomized decision making refers to the process of taking decisions randomly according to the outcome of an independent randomization device such as a dic...

Recent progress in energy storage have contributed to create large-scale storage facilities and to decrease their costs. This may bring economic opportunitie...

Stochastic programming and distributionally robust optimization seek deterministic decisions that optimize a risk measure, possibly in view of the most adv...

In this article, we discuss an alternative method for deriving conservative approximation models for two-stage robust optimization problems. The method main...

This paper proposes a multi-stage stochastic programming formulation based on affine decision rules for the reservoir management problem. Our approach seeks ...

This paper proposes a multi-stage stochastic programming formulation for the reservoir management problem. Our problem specifically consists in minimizing th...

In this paper, we study how uncertainties weighing on the climate system impact the optimal technological pathways the world energy system should take to com...

This paper presents a new formulation for the risk averse stochastic reservoir management problem. Using recent advances in robust optimization and stochasti...

Robust optimization (RO) is a powerful mean to handle optimization problems where there is a set of parameters that are uncertain. The effectiveness of the m...

Robust optimization is a methodology that has gained a lot of attention in the recent years. This is mainly due to the simplicity of the modeling process and...

Since the financial crisis of 2007-2009, there has been a renewed interest toward quantifying more appropriately the risks involved in financial positions. P...

Facility location decisions play a critical role in transportation planning. In fact, it has recently become essential to study how such commitment integrate...

Les algorithmes de simulation et régression sont désormais un outil de base dans plusieurs domaines d'application de la programmation dynamique, notamment en...

Although stochastic programming is probably the most effective frameworks for handling decision problems that involve uncertain variables, it is always a cos...

The problem of coordinating a fleet of vehicles so that all demand points on a territory are serviced and that the workload is most evenly distributed among ...