Deep Reinforcement Learning for Data-Driven Operations Management
Companies are increasingly investing in real-time tracking of logistics resources, assets and products. To monetize this investment, they must learn to continuously make optimized decisions based on the latest available information, which requires new types of algorithms. We discuss how Deep Reinforcement Learning may underlie such algorithms.
Willem van Jaarsveld is Associate Professor in Stochastic Optimization and Machine Learning at Eindhoven University of Technology (TU/e), in the OPAC group. His main research interest is stochastic optimization, using a diverse set of methodologies including Deep Reinforcement Learning, Stochastic Programming and Dynamic Programming. Applications areas include data-driven inventory control, supply chain management, and maintenance logistics.