CHAIR OF

PRODUCTION SYSTEMS

AND AUTOMATION

Current promotion (external)

Machine learning in production planning and control

PhD Supervisor: Prof. Dr. oec. Julia Arlinghaus

Production planning and control has to cope increasingly with the effects of a dynamic environment and high complexity, due to high uncertainty and the occurrence of several NP-hard problems. Since conventional PPC approaches have problems either dealing with dynamically changing problem formulations or with complexity, the effectiveness of PPC is limited in such conditions. At the same time, the increasing use of cyber-physical systems has led to a significantly larger amount of information available in near real-time that can be used for planning and control tasks. However, conventional methods of production planning and control have difficulties processing this enlarged amount of information and filtering aspects that are relevant for decision-making. Therefore, this project aims to research the extent to which production planning and control can be improved by machine learning methods and to identify barriers to its implementation.

Publications

• Konstantin Büttner, Oliver Antons, Julia C. Arlinghaus: Applied Machine Learning for Production Planning and Control: Overview and Potential. In: Manufacturing Modelling, Management and Control – 10^th MIM 2022, Nantes, June, 2022. (accepted manuscript)