Univ.-Prof. Dr.-Ing. Peter Burggräf

Deep reinforcement learning for layout planning - An MDP-based approach for the facility layout problem

Smart Containers-Enabler for More Sustainability in Food Industries?

“ReLIFE”: Business Models for Data- Based Remanufacturing: AdaptiveRemanufacturing for LifeCycle Optimisation ofNetworked CapitalGoods

A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry

Cyber-Physical Optimization of Production Processes Using Two AIs: A Robot-Guided MAG Welding Use-Case

Human trust in otherware - a systematic literature review bringing all antecedents together

Kontextabhängig Anomaliedetektion zur visuellen Hinderniserkennung für automatisierte PKW im End-of-Line-Bereich

Bridging Data Gaps In The Food Industry - Sensor-Equipped Metal Food Containers As An Enabler For Sustainability

Evaluation Of A Capacity-oriented, Agent-based Order Release For Matrix-structured Assembly Systems

Transforming Food Production: Smart Containers for Sustainable and Transparent Food Supply Chains

Application of stochastic risk simulation to increase depth of production planning

Decision support on membrane electrode assembly (MEA) production and factory concepts