A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands. - Mirko Kück; Bernd Scholz-Reiter (ICMLA (2), 2013)

Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data. - Mirko Kück; Sven F. Crone; Michael Freitag 001 (IJCNN, 2016)

Potential of data-driven simulation-based optimization for adaptive scheduling and control of dynamic manufacturing systems. - Mirko Kück; Jens Ehm; Torsten Hildebrandt; Michael Freitag 001; Enzo Morosini Frazzon (WSC, 2016)

Towards adaptive simulation-based optimization to select individual dispatching rules for production control. - Mirko Kück; Eike Broda; Michael Freitag 001; Torsten Hildebrandt; Enzo Morosini Frazzon (WSC, 2017)

Hybrid approach for the integrated scheduling of production and transport processes along supply chains. - Enzo Morosini Frazzon; André Albrecht; Matheus Cardoso Pires; Eduardo Israel; Mirko Kück; Michael Freitag 001 (Int. J. Prod. Res., 2018)

Analysis of Machine Learning Approaches to Predict Disruptions in Truck Appointment Systems. - Maurício Randolfo Flores da Silva; Mirko Kück; Enzo Morosini Frazzon; Julia Cristina Bremen (LDIC, 2024)