Jens Heger

Jens Heger is a Professor of Engineering specializing in Modeling and Simulation of Technical Systems and Processes at Leuphana University Lüneburg, Germany. He is affiliated with the Institute for Production Technology and Systems (IPTS) and the Research Center for Digital Transformation. His academic career spans over a decade of research and teaching in production engineering, manufacturing systems, and simulation technologies.

Dr. Heger completed his Master of Science in Computer Science with a minor in Business Studies at the University of Paderborn (1999-2006). He then earned his doctorate degree at the University of Bremen in the Department of Production Engineering. His dissertation focused on "Dynamic selection of rules for sequence planning in workshop and flexible flow production" under the supervision of Prof. Scholz-Reiter and Prof. Jürgen Branke from the University of Warwick, UK.

Professor Heger's research interests center around the intersection of manufacturing engineering, simulation, and artificial intelligence. His work demonstrates a strong focus on applying advanced computational methods to solve complex production challenges. Key research areas include:

• Manufacturing process optimization through simulation and modeling
• Application of machine learning and reinforcement learning in production scheduling
• Development of intelligent control systems for manufacturing processes
• Energy efficiency and sustainability in production systems
• Quality control and prediction in sheet metal forming processes
• Integration of AI technologies in traditional manufacturing environments

Analysis of Professor Heger's recent publications reveals a clear trajectory toward increasingly sophisticated applications of artificial intelligence in manufacturing. His work has evolved from traditional simulation and optimization techniques toward integrating deep learning, reinforcement learning, and computer vision technologies into production systems. A significant portion of his research focuses on dynamic scheduling and optimization problems, particularly using reinforcement learning approaches to address complex manufacturing challenges. His publications also demonstrate a growing interest in sustainable manufacturing practices, energy efficiency, and quality control applications through AI technologies.

Professor Heger has contributed significantly to the field through numerous publications in manufacturing engineering, production systems, and AI applications. His work spans both theoretical advancements in scheduling algorithms and practical implementations in industrial settings, particularly focusing on small and medium enterprises.