Portrait de Erick Delage

Erick Delage

Membre académique associé
erick.delage@hec.ca
Professeur titulaire, HEC Montréal, Département de sciences de la décision
Sujets de recherche
Apprentissage par renforcement
Optimisation
Site web
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Biographie

Erick Delage est professeur au Département de sciences de la décision à HEC Montréal, titulaire de la Chaire de recherche du Canada en prise de décision sous incertitude, et membre du Collège de nouveaux chercheurs et créateurs en art et en science de la Société royale du Canada. Ses domaines de recherche englobent l'optimisation robuste et stochastique, l'analyse de décision, l'apprentissage automatique, l'apprentissage par renforcement et la gestion des risques avec des applications en optimisation de portefeuille, en gestion des stocks, et dans les problèmes liés à l'énergie et aux transports.

Étudiants actuels

Publications

On Dynamic Program Decompositions of Static Risk Measures
Jia Lin Hau
Erick Delage
Mohammad Ghavamzadeh
Marek Petrik
Optimizing static risk-averse objectives in Markov decision processes is challenging because they do not readily admit dynamic programming d… (voir plus)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.
2022-12-31
arXiv.org (prépublication)
doi.org
Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework
Sajad Aliakbari Sani
Azadeh Maroufmashat
Frédéric Babonneau
Olivier Bahn
Erick Delage
Alain Haurie
Normand Mousseau
Kathleen Vaillancourt
More than half of the world’s population live in cities, and by 2050, it is expected that this proportion will reach almost 68%. These den… (voir plus)sely populated cities consume more than 75% of the world’s primary energy and are responsible for the emission of around 70% of anthropogenic carbon. Providing sustainable energy for the growing demand in cities requires multifaceted planning approach. In this study, we modeled the energy system of the Greater Montreal region to evaluate the impact of different environmental mitigation policies on the energy system of this region over a long-term period (2020–2050). In doing so, we have used the open-source optimization-based model called the Energy–Technology–Environment Model (ETEM). The ETEM is a long-term bottom–up energy model that provides insight into the best options for cities to procure energy, and satisfies useful demands while reducing carbon dioxide (CO2) emissions. Results show that, under a deep decarbonization scenario, the transportation, commercial, and residential sectors will contribute to emission reduction by 6.9, 1.6, and 1 million ton CO2-eq in 2050, respectively, compared with their 2020 levels. This is mainly achieved by (i) replacing fossil fuel cars with electric-based vehicles in private and public transportation sectors; (ii) replacing fossil fuel furnaces with electric heat pumps to satisfy heating demand in buildings; and (iii) improving the efficiency of buildings by isolating walls and roofs.
2022-12-31
Energies (publié)
doi.org
Network Design for Ultra-fast Delivery Services via Probabilistic Envelope Constrained Programs
Xin Wang
Okan Arslan
Jean-François Cordeau
Erick Delage
2022-12-31
SSRN Electronic Journal (publié)
doi.org
Data-Driven Optimization with Distributionally Robust Second Order Stochastic Dominance Constraints
Chun Peng
Erick Delage
This paper presents the first comprehensive study of a data-driven formulation of the distributionally robust second order stochastic domina… (voir plus)nce constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. It is, furthermore, for the first time shown to be axiomatically motivated in an environment with distribution ambiguity. We formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem. We then propose the first exact optimization algorithm for this DRSSDCP. We illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach acceptable out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy.
2022-11-21
Operational Research (publié)
doi.org