Publications from GERAD

The fruit of our labor

Cahiers du GERAD

Collection of technical reports and working papers, testimony to the strength and productivity of our group.

Scientific Publications

Repository of all the categories of scientific publications produced by our members over the years.

Newsletter

Semi-annual magazine popularizing scientific research carried out by our members and summary of our recent activities.

Recent Cahiers

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G-2026-39 Adaptive direct search algorithms with relaxable and quantifiable constraints
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This work introduces ADS-PB, an extension of the Adaptive Direct Search (ADS) framework for solving constrained blackbox optimization problems. With ADS, it...

G-2026-38 Fleet and infrastructure planning for heavy-duty electric vehicles with opportunity charging
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Heavy-duty vehicles (HDVs) are a major source of road transportation emissions, yet their electrification remains challenging due to operational constraints....

G-2026-37 Managing short-term hydropower production with a multi-agent reinforcement learning model
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Hydropower generation fulfills 94% of Québec’s electricity needs, making efficient Short-Term Hydropower Scheduling (STHS) critical for daily operations. Thi...

G-2026-36 Stochastic capacity accreditation: Incentivizing resource adequacy under weather uncertainty
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High penetrations of variable renewable energy introduce significant resource adequacy challenges, particularly when weather-driven uncertainty affects renew...

G-2026-35 Parallel versions of the mesh adaptive direct search algorithm
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This work surveys the different parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization. These problems c...

Recent Scientific Publications

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book

Pricing in the Age of AI

proceedings

Optimized FIR-Kalman Architecture for Differentially Private Event Stream Filtering
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proceedings

LQG Mean Field Games with Covariance-Matrix-Dependent Cost Coefficients
and

proceedings

Data-Driven Network LQG Mean Field Games with Heterogeneous Populations via Integral Reinforcement Learning
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