@inproceedings{MayerArnetGankinetal.2019, author = {Mayer, Sebastian and Arnet, Christian and Gankin, Dennis and Endisch, Christian}, title = {Standardized framework for evaluating centralized and decentralized control systems in modular assembly systems}, booktitle = {2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4569-3}, doi = {https://doi.org/10.1109/SMC.2019.8914314}, pages = {113 -- 119}, year = {2019}, language = {en} } @inproceedings{MayerGankinArnetetal.2019, author = {Mayer, Sebastian and Gankin, Dennis and Arnet, Christian and Endisch, Christian}, title = {Adaptive production control with negotiating agents in modular assembly systems}, booktitle = {2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4569-3}, doi = {https://doi.org/10.1109/SMC.2019.8914498}, pages = {120 -- 127}, year = {2019}, language = {en} } @article{MayerClassenEndisch2021, author = {Mayer, Sebastian and Classen, Tobias and Endisch, Christian}, title = {Modular production control using deep reinforcement learning: proximal policy optimization}, volume = {32}, journal = {Journal of Intelligent Manufacturing}, number = {8}, publisher = {Springer Nature}, address = {Cham}, issn = {1572-8145}, doi = {https://doi.org/10.1007/s10845-021-01778-z}, pages = {2335 -- 2351}, year = {2021}, abstract = {EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent's action, state, and reward design.}, language = {en} } @inproceedings{GankinMayerZinnetal.2021, author = {Gankin, Dennis and Mayer, Sebastian and Zinn, Jonas and Vogel-Heuser, Birgit and Endisch, Christian}, title = {Modular Production Control with Multi-Agent Deep Q-Learning}, booktitle = {Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-2989-1}, doi = {https://doi.org/10.1109/ETFA45728.2021.9613177}, year = {2021}, language = {en} } @inproceedings{MayerHoehmeGankinetal.2020, author = {Mayer, Sebastian and H{\"o}hme, Nikolas and Gankin, Dennis and Endisch, Christian}, title = {Adaptive Production Control in a Modular Assembly System - Towards an Agent-based Approach}, booktitle = {Proceedings, 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-2927-3}, doi = {https://doi.org/10.1109/INDIN41052.2019.8972152}, pages = {45 -- 52}, year = {2020}, language = {en} } @inproceedings{MayerEndisch2019, author = {Mayer, Sebastian and Endisch, Christian}, title = {Adaptive production control in a modular assembly system based on partial look-ahead scheduling}, booktitle = {Proceedings, 2019 IEEE International Conference on Mechatronics (ICM)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-6959-4}, doi = {https://doi.org/10.1109/ICMECH.2019.8722904}, pages = {293 -- 300}, year = {2019}, language = {en} }