Portrait of Érick Delage

Érick Delage

Associate Academic Member
Full Professor, HEC Montréal, Department of Decision Sciences


Erick Delage is a professor in the Department of Decision Sciences at HEC Montréal, the Canada Research Chair in Decision Making Under Uncertainty, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada.

Delage’s research interests span the areas of robust and stochastic optimization, decision analysis, machine learning, reinforcement learning and risk management. He focuses on the applications of these processes to portfolio optimization, inventory management, and energy and transportation problems.

Current Students

Independent visiting researcher
Postdoctorate - Université de Montréal
Principal supervisor :
Postdoctorate - HEC Montréal
PhD - HEC Montréal
PhD - HEC Montréal
PhD - HEC Montréal


Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani
Angelos Georghiou
Crowdkeeping in Last-mile Delivery
Xin Wang
Okan Arslan
A Column Generation Scheme for Distributionally Robust Multi-Item Newsvendor Problems
Shanshan Wang
This paper studies a distributionally robust multi-item newsvendor problem, where the demand distribution is unknown but specified with a ge… (see more)neral event-wise ambiguity set. Using the event-wise affine decision rules, we can obtain a conservative approximation formulation of the problem, which can typically be further reformulated as a linear program. In order to efficiently solve the resulting large-scale linear program, we develop a column generation-based decomposition scheme and speed up the computational efficiency by exploiting a special column selection strategy and stopping early based on a Karush-Kuhn-Tucker condition test. Focusing on the Wasserstein ambiguity set and the event-wise mean absolute deviation set, a computational study demonstrates both the computational efficiency of the proposed algorithm, which significantly outperforms a commercial solver and a Benders decomposition method, and the out-of-sample superiority of distributionally robust solutions relative to their sample average approximation counterparts. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [492997-2016, RGPIN-2016-05208], the National Natural Science Foundation of China [71972012], Alliance de recherche numérique du Canada, and Canada Research Chairs [CRC-2018-00105]. It was also supported by Groupe d’études et de recherche en analyse des décisions (GERAD). Finally, this research was enabled in part by support provided by Digital Research Alliance of Canada ( https://alliancecan.ca/en ). Supplemental Material: The software that supports the findings of this study is available within the paper and its supplemental information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0010 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0010 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
Distributional Robustness and Inequity Mitigation in Disaster Preparedness of Humanitarian Operations
Hongming Li
Ning Zhu
Michael Pinedo
Shoufeng Ma
Problem definition: In this paper, we study a predisaster relief network design problem with uncertain demands. The aim is to determine the … (see more)prepositioning and reallocation of relief supplies. Motivated by the call of the International Federation of Red Cross and Red Crescent Societies (IFRC) to leave no one behind, we consider three important practical aspects of humanitarian operations: shortages, equity, and uncertainty. Methodology/results: We first employ a form of robust satisficing measure, which we call the shortage severity measure, to evaluate the severity of the shortage caused by uncertain demand in a context with limited distribution information. Because shortages often raise concerns about equity, we then formulate a mixed-integer lexicographic optimization problem with nonconvex objectives and design a new branch-and-bound algorithm to identify the exact solution. We also propose two approaches for identifying optimal postdisaster adaptable resource reallocation: an exact approach and a conservative approximation that is more computationally efficient. Our case study considers the 2010 Yushu earthquake, which occurred in northwestern China, and demonstrates the value of our methodology in mitigating geographical inequities and reducing shortages. Managerial implications: In our case study, we show that (i) incorporating equity in both predisaster deployment and postdisaster reallocation can produce substantially more equitable shortage prevention strategies while sacrificing only a reasonable amount of total shortage; (ii) increasing donations/budgets may not necessarily alleviate the shortage suffered by the most vulnerable individuals if equity is not fully considered; and (iii) exploiting disaster magnitude information when quantifying uncertainty can help alleviate geographical inequities caused by uncertain relief demands. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], the National Natural Science Foundation of China [Grants 71971154, 72010107004, 72091214, and 72122015], and the Canada Research Chairs [Grant CRC-2018-00105]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1230 .
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
Jia Lin Hau
Mohammad Ghavamzadeh
Marek Petrik
Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures
Option Pricing
Saeed Marzban
Jonathan Yu-Meng Li
A double-oracle, logic-based Benders decomposition approach to solve the K-adaptability problem
A. Ghahtarani
A. Saif
A. Ghasemi
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana
Abhilash Reddy Chenreddy
Alexandre Forel
Thibaut Vidal
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani
Angelos Georghiou
The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information t… (see more)o provide more anticipative decisions. The wide range of methods and contexts of application have motivated the design of a universal unitless measure of performance known as the coefficient of prescriptiveness. This coefficient was designed to quantify both the quality of contextual decisions compared to a reference one and the prescriptive power of side information. To identify policies that maximize the former in a data-driven context, this paper introduces a distributionally robust contextual optimization model where the coefficient of prescriptiveness substitutes for the classical empirical risk minimization objective. We present a bisection algorithm to solve this model, which relies on solving a series of linear programs when the distributional ambiguity set has an appropriate nested form and polyhedral structure. Studying a contextual shortest path problem, we evaluate the robustness of the resulting policies against alternative methods when the out-of-sample dataset is subject to varying amounts of distribution shift.
Technical Note—Risk-Averse Regret Minimization in Multistage Stochastic Programs
Mehran Poursoltani
Angelos Georghiou
On Dynamic Program Decompositions of Static Risk Measures
Jia Lin Hau
Mohammad Ghavamzadeh
Marek Petrik
Optimizing static risk-averse objectives in Markov decision processes is challenging because they do not readily admit dynamic programming d… (see more)ecompositions. Prior work has proposed to use a dynamic decomposition of risk measures that help to formulate dynamic programs on an augmented state space. This paper shows that several existing decompositions are inherently inexact, contradicting several claims in the literature. In particular, we give examples that show that popular decompositions for CVaR and EVaR risk measures are strict overestimates of the true risk values. However, an exact decomposition is possible for VaR, and we give a simple proof that illustrates the fundamental difference between VaR and CVaR dynamic programming properties.