Optimal stopping is the problem of deciding the right time at which to take a particular action in a stochastic system, in order to maximize an expected rewa...

Generation expansion planning (GEP) is a classical problem that determines an optimal investment plan for existing and future electricity generation technolo...

Conditional estimation given specific covariate values (i.e., local conditional estimation or functional estimation) is ubiquitously useful with applications...

We study a distributionally robust version of the classical capacitated facility location problem with a distributional ambiguity set defined as a Wasserst...

In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the con...

Utility-based shortfall risk measure (SR) effectively captures decision maker’s risk attitude on tail losses by an increasing convex loss function. In this ...

Conditional Value at Risk (CVaR) is widely used to account for the preferences of a risk-averse agent in the extreme loss scenarios. To study the effectiven...

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...