TY - CONF A1 - Paul, Nathalie A1 - Kister, Alexander A1 - Schnellhardt, Thorben A1 - Fetz, Maximilian A1 - Hecker, Dirk A1 - Wirtz, Tim ED - Meo, Rosa ED - Silvestri, Fabrizio T1 - Reinforcement Learning for Segmented Manufacturing N2 - The manufacturing of large components is, in comparison to small components, cost intensive. This is due to the sheer size of the components and the limited scalability in number of produced items. To take advantage of the effects of small component production we segment the large components into smaller parts and schedule the production of these parts on regular-sized machine tools. We propose to apply and adapt recent developments in reinforcement learning in combination with heuristics to efficiently solve the resulting segmentation and assignment problem. In particular, we solve the assignment problem up to a factor of 8 faster and only a few percentages less accurate than a classic solver from operations research. T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023 CY - Turin, Italy DA - 18.09.2023 KW - Reinforcement Learning KW - Assignment Problem KW - Large component manufacturing PY - 2025 DO - https://doi.org/10.1007/978-3-031-74640-6_38 VL - 1 IS - 1 SP - 470 EP - 485 PB - Springer Cham AN - OPUS4-63031 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -