Recently there has been a surge of interest in operations research~(OR) and the machine learning~(ML) community in combining prediction algorithms and optimi...

Optimizing static risk-averse objectives in Markov decision processes is challenging because they do not readily admit dynamic programming decompositions. Pr...

In order to improve the efficiency of the last-mile delivery system when customers are possibly absent for deliveries, we propose the idea of employing the c...

In this paper, we study a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Specifically, we addres...

This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the adverti...

Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations ei...

We study a predisaster relief network design problem with uncertain demands. The aim is to determine the prepositioning and reallocation of relief supplies. ...

The problem of portfolio management represents an important and challenging class of dynamic decision making problems, where rebalancing decisions need to be...

Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. In...

Within the context of optimization under uncertainty, a well-known alternative to minimizing expected value or the worst-case scenario consists in minimizing...

In this paper, we study a distributionally robust multi-item newsvendor problem, where the demand distribution is unknown but specified with a general event-...

Battery charging of electric vehicles (EVs) needs to be properly coordinated by electricity producers to maintain the network reliability. In this paper, we ...

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...

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 ...