Stefan Minner is a Full Professor for Logistics and Supply Chain Management at the School of Management, Technical University of Munich (TUM) and a core member of the Munich Data Science Institute (MDSI). His research interests are in global supply chain design, transportation optimization and inventory management using Operations Research and Machine Learning methods. The work was published in many peer reviewed journals, including Management Science, Manufacturing & Service Operations Management, Operations Research, Production and Operations Management, Transportation Science, Transportation Research Part B, European Journal of Operational Research, Computers and Operations Research and OR Spectrum. He serves on several editorial boards of logistics and operations journals. Currently, Stefan Minner is the Editor-in-Chief of the International Journal of Production Economics and of Sustainable Manufacturing and Service Economics. Stefan Minner is a fellow of the International Society for Inventory Research (ISIR) and is currently vice-chairman of the scientific advisory board of the German Logistics Association (BVL), and a member of the Research Committee of the European Logistics Association (ELA).
Advanced Optimization in a Networked Economy (AdONE): DFG Research Training Group GRK 2201, www.gs.tum.de/en/adone/start/
Digital Logistics Laboratory: The use of Artificial Intelligence algorithms is seen as a major acceleration in logistics optimization, it complements classical Operations Research approaches. We combine fundamental research on machine learning algorithms with logistics domain knowledge, apply competitive reinforcement learning for real-time optimization, and promote data-driven approaches for integrated forecasting and decision making.
City Crowd Logistics and Radlast: Last-mile delivery is the most complex and costly part of goods distribution in urban logistics and has received increasing importance due to growth of e-commerce. We develop innovative business and organization models for designing platforms to crowd source and optimize last-mile services.
Data-driven Collaboration in Industrial Supply Chains: New disruptive business models in freight transportation that need decision support. Collaborations often require the sharing of large amounts of data and centralized planning, which raises concerns and hinders adoption. We develop decentralized planning approaches with limited data-sharing requirements by using encryption and edge computing to take advantage of the benefits of collaborative logistics.
Integrated Learning and Optimization for Mobility and Transportation Services: New business models in transportation are exposed to a large degree of uncertainty at the stage of design and operations due to limited available historical information. We build on concepts from data-driven optimization, stochastic programming and machine learning to develop decision support with the application to transportation and mobility, in particular for bike- and car-sharing.
