TY - RPRT A1 - Andrae, Marc A1 - Auer, Veronika A1 - Barth, Simon A1 - Baumann, Sarah A1 - Betz, Andreas A1 - Châteauvieux-Hellwig, Camille A1 - Geladze, Ekaterine A1 - Hauch, Julia A1 - Hirschmüller, Sebastian A1 - Kapitza, Stephanie A1 - Karlinger, Peter A1 - Köster, Heinrich A1 - Kramer, Oliver A1 - Lüftl, Katharina A1 - Martius, Henrike A1 - Mayr, Andreas A1 - Michanickl, Andreas A1 - Miletzky, Frank A1 - Mühlbauer, Wolfgang A1 - Niedermaier, Peter A1 - Obermeier, Frederik A1 - Peklo, Franziska A1 - Regauer, Verena A1 - Schanda, Ulrich A1 - Schemme, Michael A1 - Schiffer, Lukas A1 - Schöpfer, Fabian A1 - Schumacher, Mara A1 - Schutter, Sabina A1 - Schweiger, Andreas A1 - Seckler, Eva A1 - Sigg, Ferdinand T1 - Jahresbericht 2019, Forschung - Entwicklung - Innovation N2 - Mit dem jährlich erscheinenden Forschungsbericht möchte die Technische Hochschule Rosenheim einen Einblick in ihre vielfältigen Projekte und Aktivitäten der angewandten Forschung und Entwicklung geben. T3 - Schriftenreihen - Forschungsbericht - 8 KW - Forschung Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:861-opus4-13977 ER - TY - INPR A1 - Böhm, Stefan-Andreas A1 - Neumayer, Martin A1 - Kramer, Oliver A1 - Schiendorfer, Alexander A1 - Knoll, Alois T1 - Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem N2 - Cutting and Packing problems are occurring in different industries with a direct impact on the revenue of businesses. Generally, the goal in Cutting and Packing is to assign a set of smaller objects to a set of larger objects. To solve Cutting and Packing problems, practitioners can resort to heuristic and exact methodologies. Lately, machine learning is increasingly used for solving such problems. This paper considers a 2D packing problem from the furniture industry, where a set of wooden workpieces must be assigned to different modules of a trolley in the most space-saving way. We present an experimental setup to compare heuristics, constraint optimization, and deep reinforcement learning for the given problem. The used methodologies and their results get collated in terms of their solution quality and runtime. In the given use case a greedy heuristic produces optimal results and outperforms the other approaches in terms of runtime. Constraint optimization also produces optimal results but requires more time to perform. The deep reinforcement learning approach did not always produce optimal or even feasible solutions. While we assume this could be remedied with more training, considering the good results with the heuristic, deep reinforcement learning seems to be a bad fit for the given use case. KW - Computer Science - Artificial Intelligence Y1 - 2021 U6 - https://doi.org/10.48550/arXiv.2110.14535 ER -