@inproceedings{MillerKauppSchmitt, author = {Miller, Eddi and Kaupp, Tobias and Schmitt, Jan}, title = {Cascaded Scheduling for Highly Autonomous Production Cells with AGVs}, series = {Manufacturing Driving Circular Economy: Proceedings of the 18th Global Conference on Sustainable Manufacturing, October 5-7, 2022, Berlin ; Lecture Notes in Mechanical Engineering}, booktitle = {Manufacturing Driving Circular Economy: Proceedings of the 18th Global Conference on Sustainable Manufacturing, October 5-7, 2022, Berlin ; Lecture Notes in Mechanical Engineering}, editor = {Kohl, Holger and Seliger, G{\"u}nther and Dietrich, Franz}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-28838-8}, doi = {https://doi.org/10.1007/978-3-031-28839-5_43}, pages = {383 -- 390}, abstract = {Highly autonomous production cells are a crucial part of manufacturing systems in industry 4.0 and can contribute to a sustainable value-adding process. To realize a high degree of autonomy in production cells with an industrial robot and a machine tool, an experimental approach was carried out to deal with numerous challenges on various automation levels. One crucial aspect is the scheduling problem of tasks for each resource (machine tool, tools, robot, AGV) depending on various data needed for a job-shop scheduling algorithm. The findings show that the necessary data has to be derived from different automation levels in a company: horizontally from ERP to shop-floor, vertically from the order handling department to the maintenance department. Utilizing that data, the contribution provides a cascaded scheduling approach for machine tool jobs as well as CNC and robot tasks for highly autonomous production cells supplied by AGVs.}, language = {en} }