This study explores accelerating strategies in column generation (CG) to effectively solve online dial-a-ride problems in large-scale ride-sharing systems. ...

This paper addresses the scheduling of electrical drilling machines in open-pit mines, proposing three constraint programming formulations to account for unc...

This article addresses the efficient coordination of multiple electrical drill rigs in open-pit mines, considering unique constraints such as continuous powe...

The Thirteenth Montreal IPSW took place on August 21-25, 2023, and was jointly organized by the Centre de recherches mathématiques (CRM) and the Institute ...

In this work, we study a class of two-player deterministic finite-horizon difference games with coupled inequality constraints, where both players have two t...

In this work, we improve the efficiency of Unit Commitment (UC) optimization solvers using a Graph Convolutional Neural Network (GCNN). In power systems, UC ...

We consider the set of graphs that can be constructed from a one-vertex graph by repeatedly adding a clique or a stable set linked to all or none of the vert...

Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential community microgrid (RCM)...

Recommender systems provide personalized recommendations to their users for items and services. They do that using a model that is tailored to each user to i...

We propose new algorithms with provable performance for online binary optimization subject to general constraints and in dynamic settings. We consider the su...

In this paper, a new model is proposed for the real-time diesel genset optimal dispatch and unit commitment in remote microgrids. The objective is to reduce ...

The Twelfth Montreal IPSW took place on August 22-26, 2022, and was jointly organized by the Centre de recherches mathématiques (CRM) and the Institute for D...

An innovative simultaneous stochastic optimization approach is proposed that combines actor-critic reinforcement learning and stochastic mathematical progr...

In multi-robot missions, relative position and attitude information between robots is valuable for a variety of tasks such as mapping, planning, and formatio...

Recommender systems provide recommendations to their users for items and services by creating a model tailored to each user to infer their preferences based ...

Motivated by our collaboration with one of the largest fast-fashion retailers in Europe, we study a two-echelon inventory control problem called the One-Ware...

We consider online optimization problems with time-varying linear equality constraints. In this framework, an agent makes sequential decisions using only pri...

Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modi...

Hybrid renewable energy systems (HRES), which co-locate two or more renewable energy sources, have proven to be promising frameworks for harnessing complemen...

Thermostatically-controlled loads have a significant impact on electricity demand after service is restored following an outage, a phenomenon known as cold l...