Optimizing Supply Chains with GNN

Thibaut Vidal, a professor at Polytechnique Montreal, specializes in leveraging advanced algorithms and machine learning to optimize supply chain operations. In this episode, listeners will learn how graph-based approaches can transform supply chains by enabling more efficient routing, districting, and decision-making in complex logistical networks. Key insights include the application of Graph Neural Networks to predict delivery costs, with potential to improve districting strategies for companies like UPS or Amazon and overcoming limitations of traditional heuristic methods. Thibaut’s work underscores the potential for GNN to reduce costs, enhance operational efficiency, and provide better working conditions for teams through improved route familiarity and workload balance.

Guest

Thibaut Vidal: Thibaut Vidal holds the SCALE-AI Chair in Data-Driven Supply Chains and is a professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. His expertise lies in combinatorial optimization and trustworthy machine learning, with applications spanning transportation, supply chain management, resource allocation, and information processing. Dr. Vidal has authored over 50 peer-reviewed studies published in leading journals and conferences in operations research and machine learning. His academic contributions include developing state-of-the-art algorithms, many of which are accessible through open-source libraries, facilitating advancements in data-driven logistics operations. He has also collaborated with various companies to enhance data analytics and logistics performance through consulting projects. Among others, he has been the recipient of two best paper awards from the Transportation Science and Logistics section of INFORMS, as well as the Robert Faure prize from the French Operations Research society.