Michel Gendreau

Papers

Total Citations

H-Index

About

Michel Gendreau is a prominent operations research scholar whose recent work has focused on the rapidly evolving intersection of robotics, automation, and e-commerce logistics. His research centers on Robotic Mobile Fulfillment Systems (RMFSs), a cutting-edge class of automated warehouse technology increasingly deployed by major online retailers to meet the demands of modern supply chains. Gendreau's most influential contribution in this space, a 2021 mathematical modelling framework for RMFSs in e-commerce applications, has garnered 37 citations, establishing itself as a foundational reference for researchers and practitioners navigating the complexity of parts-to-picker robotic systems. Complementing this, his work on learning-based storage policies explores how machine learning can be leveraged to optimize warehouse operations dynamically, reflecting a forward-thinking integration of artificial intelligence with combinatorial optimization. A third study applies supervised learning and tree search methods to real-time storage allocation, addressing the operational challenges that arise when numerous robots operate simultaneously in shared environments. Together, these contributions position Gendreau at the forefront of intelligent warehouse automation research, offering rigorous analytical tools and adaptive decision-making frameworks that are shaping the future of e-commerce fulfillment infrastructure.