Supply Chains and Mobility Systems

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.

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
• 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

Dana F. Simon: dana.simon@ivado.ca