Supply Chains and Mobility Systems

IVADO Research Regroupement 9 is launching a research scholarship programme to provide a supplement of financial support to PhD students undertaking high impact research on topics related to the development and application of AI methods to supply chains and mobility systems. Funding of up to $10,000 is available for a one-year, non-renewable period. Learn more >>

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

Dynamic Demand and Capacity Management for B2B Transportation Services

(Axes 1, 3 et 4)

Emma Frejinger – Université de Montréal
Yossiri Adulyasak – HEC Montréal
Jean-François Cordeau – HEC Montréal

Efficient Covariance Modeling for High-Dimensional Spatiotemporal Forecasting

(Axes 1 et 2)

Lijun Sun – McGill University
Youssef Diouane – Polytechnique Montréal

Reducing Cart Abandonment through Dynamic Learning and Optimization of Shipping Policies in E-Commerce Platforms

Mehmet Gumus – McGill University
Rim Hariss – McGill University
Recep Bekci – University of Waterloo)
Sentao Miao – University of Colorado, Boulder

Responsible Network Design Problem

(Axes 3 et 5)

Okan Arslan – HEC Montréal
Yossiri Adulyasak – HEC Montréal
Margarida Carvalho – Université de Montréal

Sample-efficient uncertainty quantification through physics-informed learning for building large-scale urban mobility digital twins

(Axes 1 et 2)

Youssef Diouane – Polytechnique Montréal
Carolina Osorio – HEC Montréal
Lijun Sun – McGill University

Multi-dimensional Distribution Learning for Supply Chain Management

(Axes 1 et 4)

Erick Delage – HEC Montréal

Large language model agent for mobility reasoning and synthesis

Lijun Sun – McGill University

Learning human preferences for optimization under endogenous uncertainty

Emma Frejinger – Université de Montréal

General risk-aware reinforcement learning algorithms

Erick Delage – HEC Montréal

Apprentissage et optimisation intégrés sous incertitude endogène

Utsav Sadana – Université de Montréal

AI-driven information design for competitive learning in digital marketplaces

Mehmet Gumus – McGill University

Decision-aware learning for supply chains and industrial systems: balancing computational efficiency and solution reliability

Erick Delage – HEC Montréal

Data-driven multi-stage optimization under endogenous demand in size inclusive fashion retail

Mehmet Gumus – McGill University

Fair Sequential Decision-Making in Weakly Coupled Markov Decision Processes

Erick Delage – HEC Montréal

Application-Driven Tree Approximation for Multistage Stochastic Programs

Maryam Daryalal – HEC Montréal

Learning Road Network Distances for Combinatorial Optimization

Okan Arslan – HEC Montréal

Understanding customer decisions under stock-out endogeneity and product uncertainty in Omnichannel retail

Sanjith Gopalakrishnan – McGill University

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)