Wuttke, A.; Rabe, M.; Hunker, J.; Diepenbrock, J.-P.:Combining Simulation and Recurrent Neural Networks for Model-based Condition Monitoring of Machines. In Lam, H.; Azar, E.; Batur, D.; Gao, S.; Xie, W.; Hunter, S.R.; Rossetti, M.D. (Hrsg.): Proceedings of the 2024 Winter Simulation Conference, Piscataway, NJ: IEEE, S. 1551-1562. Details.

Wuttke, A.; Hunker, J.; Rabe, M.: LogFarm: An Open Source Graph-based Simulator for Logistics Networks. SNE 34 (2024) 1, S. 43-50. Details.

Wuttke, A.; Hunker,J,; Rabe, M.; Diepenbrock, J.-P.: Estimating Parameters with Data Farming for Condition-based Maintenance in a Digital Twin. In: Corlu, C.G.; Hunter, S.R.; Lam, H.; Onggo, S.; Shortle, J.; Biller, B. (eds.): Proceedings of the Winter Simulation Conference 2023. Piscataway, NJ: IEEE 2023, pp. 1641-1652. Details.

Wuttke, A.; Hunker, J.; Scheidler, A. A.; Rabe, M.: Synthetic Demand Generation with Seasonality for Data Mining on a Data-Farmed Data Basis of a Two-Echelon Supply Chain. Procedia Computer Science 204 (2022), pp. 226-234 Details.

Rabe, M.; Klüter, A.; Wuttke, A.: Evaluating the Consolidation of Distribution Flows Using a Discrete Event Supply Chain Simulation Tool: Application to a Case Study in Greece. . In: Rabe, M,; Juan, A.A.; Mustafee, N.; Skoogh, A.; Jain, S.; Johansson, B. (eds.): Proceedings of the 2018 Winter Simulation Conference. Picataway: IEEE 2018, pp. 2815-2826. Details.

Rabe, M.; Dross, F.; Wuttke, A: Combining a Discrete-event Simulation Model of a Logistics Network with Deep Reinforcement Learning. In: Duarte, A.; Juan, A.A.; Mélian, B.; Ramalhinho, H. (eds.): Metaheuristics: Proceeding of the MIC and MAEB 2017 Conferences, Barcelona, July 4th-7th, 2017, pp. 765-774. Details.

Cesen, M.: Entwicklung eines Konzepts für eine Datenbasis zur zustandsorientierten Instandhaltung von Industrieöfen in einem digitalen Zwilling basierend auf Sensordaten. Technische Universität Dortmund, Fachgebiet IT in Produktion und Logistik, Masterarbeit, TU Dortmund University, Department IT in Production and Logistics, master thesis, 2024.

Cetinel, C.: Entwicklung eines neuronalen Netzes zur Anomalieerkennung in Sensordaten von Industrieöfen. TU Dortmund University, Department IT in Production and Logistics, master thesis, 2024.

Bin Afsar, R.: Comparison of Machine Learning Approaches for Anomaly Detection on Industrial Furnace. TU Dortmund University, Department IT in Production and Logistics, project thesis, 2023.

Fuest, T.: Erfassung und Verarbeitung von Vibrationsdaten zur präventiven Instandhaltung von Industrieöfen. TU Dortmund University, Department IT in Production and Logistics, master thesis, 2023.

Vedder, N. L.: Systematische Untersuchung von Sensorik zur Erfassung von Daten über Heizräume von Industrieöfen. TU Dortmund University, Department IT in Production and Logistics, bachelor thesis, 2023.