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Recent developments in machine learning and optimization have the potential to enable supply chain decision-makers and practitioners to leverage data and deal with uncertainty in an effective and adaptive fashion. This scientific workshop presents various industrial realizations demonstrating how modern machine learning and optimization methods can effectively be applied in practice. Through academic-style talks from speakers in both academia and industry, they will present various use cases in supply chains and scientific approaches, as well as challenges and opportunities in the applications of learning and optimization methods. There will also be dedicated time for informal discussions among participants.

The conference provides coffee, lunch and post-event cocktail reception with finger foods.

Titles and speakers:

• Practical Considerations for Decision Under Uncertainty in an Industrial Setting
  Marie-Claude Côté, Sacha Izadi, Nicolas Boez, Jean-François Landry (IVADO Labs)

• Sample Efficient Algorithms for Urban Mobility Problems Through Physics-Informed Machine Learning
  Carolina Osorio (HEC Montréal and Google Research)

• Ship Route Optimization - Multi-Objective Approach
  Capt. Gurjeet Warya (True North Marine)

• Humanitarian Supply Chain Analytics
  Marie-Ève Rancourt (HEC Montréal)

• Operational Design and Data in Smallholder Farmer Supply Chains
  Joann de Zegher (PemPem)

• Optimize-Then-Predict: An Imitation-Based Learning Framework
  Louis-Martin Rousseau (Polytechnique)

• Unlocking Decision-Making under Uncertainty: The Power of Industry-Academia Collaboration
  Emma Frejinger (Université de Montréal), Helen Glover (CN)

• Leveraging Machine Learning and Optimization in Supply Chain Planning
  Yossiri Adulyasak (HEC Montréal)

Full program here