Maximilian Schiffer studied business administration and electrical power engineering at RWTH Aachen University and received his doctoral degree in operations research from RWTH Aachen in 2017. In 2018 he held a postdoctoral position at RWTH Aachen University and a visiting scholar position in the Autonomous Systems Laboratory at Stanford University. Max is an associate member of the GERAD, Canada. In September 2018, Max was appointed as a faculty member in the Operations and Supply Chain Management Department of TUM School of Management and in TUM’s newly created Center for Energy Markets. Max’s research spans different fields of operations research and management. These include a wide range of transportation and logistics topics, e.g., electric vehicles and autonomous systems, but also topics from production planning, supply chain management, and data science. In these fields, Max develops state-of-the-art algorithms that are suitable for real-world applications.
Autonomous Mobility on Demand Systems: We study the impact of autonomous mobility on demand (AMoD) systems on city logistics and passenger transportation. Herein, we develop mathematical models and algorithms from a system perspective to assess potential benefits but also develop real-time algorithms. Further, we focus on smart grid topics and develop models that consider the interdependencies between an AMoD system and the power network.
Warehouse Operations: The importance of efficiently operating a warehouse became increasingly important in recent years, especially with the rise of e-commerce. We develop the first generic exact algorithms for picker routing in warehouses that can be operated in real-world warehouses of online retailers and show computational times of only a few seconds. Further, we develop similar algorithms for robotized warehouses and analyze design aspects in warehouses in general.
Spatio-Temporal Forecasting: We enhance neural networks usually used for pattern recognition to be suitable for Spatio-temporal forecasts in every kind of system. By doing so, we succeed in capturing a new information density in modern forecasting techniques.
Value-based Production Planning: We develop value-based end-to-end production planning systems to maximize a company’s profit which is not necessarily correlated with its throughput. Our models and algorithms are applied in practice in the copper industry.
