# Erick Delage

Back## Publications

### Cahiers du GERAD

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

BibTeX reference**Erick Delage**, Mohammad Ghavamzadeh, and Marek Petrik

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

BibTeX referenceCrowdkeeping in last-mile delivery

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...

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

BibTeX reference**Erick Delage**

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

BibTeX referenceDeep reinforcement learning for option pricing and hedging under dynamic expectile risk measures

**Erick Delage**, and Jonathan Y. Li

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

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

BibTeX reference**Erick Delage**, Jonathan Y. Li, Jeremie Desgagne-Bouchard, and Carl Dussault

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

BibTeX referenceData-driven optimization with distributionally robust second-order stochastic dominance constraints

**Erick Delage**

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

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

BibTeX reference**Erick Delage**

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

BibTeX referenceRobust integration of electric vehicles charging load in smart grids capacity expansion planning

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

BibTeX referenceDeep reinforcement learning for optimal stopping with application in financial engineering

**Erick Delage**

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...

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

BibTeX reference**Erick Delage**, and Yinyue Ye

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

BibTeX reference**Erick Delage**

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

**Erick Delage**, and Jonathan Y. Li

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...

BibTeX reference**Erick Delage**, Shaoyan Guo, and Huifu Xu

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

BibTeX referenceThe value of randomized strategies in distributionally robust risk averse network interdiction games

**Erick Delage**

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...

BibTeX referenceAdjustable robust optimization reformulations of two-stage worst-case regret minimization problems

**Erick Delage**

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

BibTeX reference**Erick Delage**

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...

**Erick Delage**

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

BibTeX reference**Erick Delage**, and Jinlin Li

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

BibTeX referenceThe value of randomized solutions in mixed-integer distributionally robust optimization problems

**Erick Delage**and Ahmed Saif

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

Robust self-scheduling of a price-maker energy storage facility in the New York electricity market

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

BibTeX reference**Erick Delage**, Daniel Kuhn, and Wolfram Wiesemann

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

**Erick Delage**

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

BibTeX reference**Erick Delage**, and Michel Gendreau

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

BibTeX referenceA stochastic program with time series and affine decision rules for the reservoir management problem

**Erick Delage**, and Michel Gendreau

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

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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...

BibTeX reference**Erick Delage**, and Michel Gendreau

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

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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...

BibTeX referenceRobust optimization of sums of piecewise linear functions with application to inventory problems

**Erick Delage**

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...

BibTeX reference**Erick Delage**and Jonathan Y. Li

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

BibTeX reference**Erick Delage**

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

BibTeX reference**Erick Delage**, Michel Denault, and Jean-Guy Simonato

Simulation-and-regression algorithms have become a standard tool for solving dynamic programs in many areas, in particular financial engineering and computat...

BibTeX reference**Erick Delage**, Sharon Arroyo, and Yinyu Ye

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

BibTeX reference**Erick Delage**

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 ...

BibTeX reference### Articles

**Erick Delage**, and Jonathan Y. Li

**Erick Delage**, and Jonathan Y. Li

**Erick Delage**

**Erick Delage**

**Erick Delage**, Ning Zhu, Michael Pinedo, and Shoufeng Ma

**Erick Delage**

**Erick Delage**, and Angelos Georghiou

**Erick Delage**, Shaoyan Guo, and Huifu Xu

**Erick Delage**, and Rinel Foguen Tchuendom

**Erick Delage**, Alain Haurie, Normand Mousseau, and Kathleen Vaillancourt

**Erick Delage**, and Jonathan Y. Li

**Erick Delage**

**Erick Delage**

**Erick Delage**

**Erick Delage**

**Erick Delage**, and Jinlin Li

**Erick Delage**, and Morad Abdelaziz

**Erick Delage**, Daniel Kuhn, and Wolfram Wiesemann

**Erick Delage**, and Kristen R. Schell

**Erick Delage**, and Michel Gendreau

**Erick Delage**, Luca G. Gianoli, and Brunilde Sansò

**Erick Delage**, and Michel Gendreau

**Erick Delage**, and Michel Gendreau

**Erick Delage**, and Jean-Guy Simonato

**Erick Delage**

**Erick Delage**, Sharon Arroyo, and Yinyu Ye

**Erick Delage**, and Abdel Lisser

**Erick Delage**, Mark Peters, Zizhuo Wang, and Yinyu Ye

**Erick Delage**and Yinyu Ye

**Erick Delage**and Shie Mannor

### Proceedings

**Erick Delage**

**Erick Delage**

**Erick Delage**, and Yinyu Ye

**Erick Delage**

**Erick Delage**

**Erick Delage**, Mark Peters, Zizhuo Wang, and Yinyu Ye