Integrate modern optimization and machine learning methods to improve the efficiency and resilience of supply chains and mobility systems.
Vision
In a world shaped by ever-changing global uncertainties, this Regroupement aims to rethink the management of supply chains and mobility systems by integrating operational research and machine learning. It aims to make these systems more robust, responsive and sustainable, while reducing their environmental footprint and strengthening Quebec’s leadership in the integration of these fields.
Objectives
The objectives of this Regroupement are to integrate and improve modern optimization and machine learning methods to improve the efficiency and resilience of supply chains and mobility systems, while reducing their environmental footprint. The goal is to exploit machine learning:
- In the design of optimization models that will make these systems more reactive to changing conditions.To accelerate the optimization of very large systems that account for economic, societal, environmental and human contexts.
- To quantify uncertainty and make supply chains more resistant to disruptions caused by natural disasters, pandemics, wars, and other global events.
Research Axes
Axis 1: Complex uncertainty quantification
Out-of-distribution robustness, count data predictions, complex correlation structure, learning under partial observability.
Axis 2: Integrating prediction and optimization
Simulated environments for contextual optimization, robust supply chain optimization, cost-effective vessel routing and scheduling.
Axis 3: Accelerating the solution of multi-stage stochastic decision problems
Machine learning-assisted Bender decomposition, reinforcement learning for inventory management, fleet management for rail transportation.
Axis 4: Developing decision-aware end-to-end optimization
Fair decision-making, learning of risk-averse policies, integration of human behavior.
Axis 5: Integrating endogeneity in decision models
Integrated learning and optimization for demand forecasting, mitigating epistemic uncertainty in offline learning.
Challenges
Supply chains and mobility systems are facing unprecedented volatility due to global crises, such as pandemics or natural disasters. How can we develop solutions to better anticipate and respond to these disruptions? The challenge also lies in striking a balance between operational efficiency, transparent decision-making, and stakeholder satisfaction, while minimizing environmental impact.
Anticipated Impact
The Regroupement ‘s activities will contribute to three main dimensions of R³AI initiative by developing robust, reasoning, and responsible artificial intelligence (AI) systems. There is a strong need to have robust systems that can react quickly to unforeseen changes and mitigate the effects of these disruptions on downstream operations.
Reasoning systems, creating systems that are more stable and less sensitive to data variations, that are more similar in their behavior to how humans reason and make decisions. The Regroupement will contribute to the responsible adoption of AI by developing models and algorithms that lead to decisions that achieve good trade-offs between the objectives and constraints of multiple stakeholders.
- Scientific progress: significant scientific contributions to the related disciplines and diffusion with publications in top-tier scientific journals, conferences, etc.
- Methodological developments: tests on real data in order to draw relevant practical and managerial insights.
- Industrial collaboration: implementing innovations with strategic players that will ensure methods can be adopted in a real-world setting.
Ongoing Projects
AI-Driven Optimization for Multimodal Transportation Networks: Integrating Theoretical Insights with Data-Driven Decision Modeling
(Axis 4)
- Bobin Wang – Université Laval
- Christian Gagné – Université Laval
- Mikael Rönnkvist – Université Laval
Gamifying online stochastic optimization problems
(Axis 2)
- Jorge Mendoza – HEC Montréal
- Martin Cousineau – HEC Montréal
- Amir-Massoud Farahmand – Polytechnique Montréal
Uncertainty Quantification for Spatiotemporal and Multivariate Demand in Supply Chain and Mobility System
(Axis 1)
- Lijun Sun – McGill University
- Alexandra Schmidt – McGill University
Dynamic Data-Driven Resource Planning for Wildfire Management under Uncertainty
(Axis 1)
- Marie-Ève Rancourt – HEC Montréal
- Daniel Aloise – Polytechnique Montréal
- Valérie Bélanger – HEC Montréal
- Fannie Couture – HEC Montréal
Learning-based sequential decision-making under uncertainty
(Axis 3)
- Maryam Daryalal – HEC Montréal
- Erick Delage – HEC Montréal
RETRO: Optimization of Orbital Debris Remediation through Machine Learning and Combinatorial Optimization
(Axis 2, Axis 4)
- Thibaut Vidal – Polytechnique Montréal
- Christopher Damaren – University of Toronto
- Sanjeev Narayanaswamy – University of Toronto
Development of machine learning methods and stochastic short-term production planning to reduce environmental footprint of dynamic fleet management systems in mining complexes
(Axis 2)
- Roussos Dimitrakopoulos – McGill University
- Luis Montiel Petro – McGill University
- Amina Lamghari – Université du Québec à Trois-Rivières
- Yassine Yaakoubi – Concordia University
Efficient Risk-averse Policy Learning for Supply Chain Management
(Axis 4)
- Erick Delage – HEC Montréal
- Yossiri Adulyasak – HEC Montréal
- Nima Akbarzadeh – HEC Montréal
- Pierre-Luc Bacon – Université de Montréal
- Esther Derman – Université de Montréal
- Petrik Marek – University of New Hampshire
- Weikai Wang – HEC Montréal
Counterfactual explanations for contextual optimization problems subject to endogenous uncertainty
(Axis 2, Axis 5)
- Emma Frejinger – Université de Montréal
Data-driven Optimization for Inventory Systems under Censored Demand
(Axe 2)
- Yossiri Adulyasak – HEC Montréal
- Jean-François Cordeau – HEC Montréal
Research Team
Co-leaders
HEC Montréal
HEC Montréal
HEC Montréal
Université de Montréal
Researchers
- Okan Arslan – HEC Montréal
- Pierre-Luc Bacon – Université de Montréal
- Valérie Bélanger – HEC Montréal
- Glen Berseth – Université de Montréal
- Margarida Carvalho – Université de Montréal
- Laurent Charlin – HEC Montréal
- Leandro Coelho – Université Laval
- Maxime Cohen – McGill University
- Maryam Darvish – Université Laval
- Maryam Daryalal – HEC Montréal
- Guy Desaulniers – Polytechnique Montréal
- Roussos Dimitrakopoulos – McGill University
- Michel Gendreau – Polytechnique Montréal
- Raf Jans – HEC Montréal
- Luc LeBel – Université Laval
- Antoine Legrain – Polytechnique Montréal
- Nadia Lehoux – Université Laval
- Aditya Mahajan – McGill University
- Jorge Mendoza – HEC Montréal
- Carolina Osorio – HEC Montréal
- Marie-Ève Rancourt – HEC Montréal
- Jacques Renaud – Université Laval
- Mikael Rönnkvist – Université Laval
- Louis-Martin Rousseau – Polytechnique Montréal
- Utsav Sadana – Université de Montréal
- Dhanya Sridhar – Université de Montréal
- Lijun Sun – McGill University
- Kimberley Yu – Université de Montréal
- Thibaut Vidal – Polytechnique Montréal
- Bobin Wang – Université Laval
Research Advisor
Danielle Maia de Souza : danielle.maia.de.souza@ivado.ca