I regularly speak at national and international research conferences as well as industry workshops. Most of the talks dealt with simulation-based optimization methods for adaptive production control, automatic forecasting methods for customer demands, machine learning methods, statistical learning methods and neural networks in industrial applications as well as modeling and simulation for production and logistics systems. Below, you can find a selection of talks in English and in German.
- Towards Adaptive Simulation-Based Optimization to Select Individual Dispatching Rules for Production Control. 2017 Winter Simulation Conference, IEEE, December 3-6, 2017, Las Vegas, NV, USA.
- An adaptive simulation-based optimization approach for the scheduling and control of dynamic manufacturing systems - Current state of the project. 9th Annual Meeting of the Brazilian-German Research Initiative on Manufacturing Technology (BRAGECRIM), November 8-10, 2017, Salvador, Bahia, Brazil.
- Potential of Data-Driven Simulation-Based Optimization for Adaptive Scheduling and Control of Dynamic Manufacturing Systems. 2016 Winter Simulation Conference, IEEE, December 11-14, 2016, Washington, D.C., USA.
- An adaptive simulation-based optimization approach for the scheduling and control of dynamic manufacturing systems - Current state of the project. 8th Annual Meeting of the Brazilian-German Research Initiative on Manufacturing Technology (BRAGECRIM), November 15-16, 2016, Bremen, Germany.
- A data-driven simulation-based optimization approach for adaptive scheduling and control of dynamic manufacturing systems. 6. WGP-Jahreskongress, WGP, September 5-6, 2016, Hamburg, Germany.
- Forecasting Model Selection for Industry Data by Applying Meta-Learning with Feature Selection. OR2016 – Annual International Conference of the German Operations Research Society, GOR, August 30 - September 2, 2016, Hamburg, Germany.
- Meta-Learning with Neural Networks and Landmarking for Forecasting Model Selection - An Empirical Evaluation of Different Feature Sets Applied to Industry Data. 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, July 24-29, 2016, Vancouver, BC, Canada.
- Forecasting model selection for industry data by applying meta-learning with different feature sets. 36th International Symposium on Forecasting, IIF, June 19-22, 2016, Santander, Spain.
- Prediction of customer demands for production planning - Automated selection and configuration of suitable prediction methods. CIRP General Assembly, CIRP, August 24-30, 2014, Nantes, France.
- Robust Methods for the Prediction of Customer Demands Based on Nonlinear Dynamical Systems. 2nd CIRP Robust Manufacturing Conference (RoMac 2014), CIRP, July 7-9, 2014, Bremen Germany.
- Automated Forecasting of Univariate Time Series Based on Classification. 34th International Symposium on Forecasting, IIF, June 29 - July 2, 2014, Rotterdam, Netherlands.
- Forecasting of Customer Demands in Production Networks Based on Phase Space Reconstruction - An application to predict intermittent demand evolutions. 33rd International Symposium on Forecasting, IIF, June 23-26, 2013, Seoul, South Korea.
- A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands. 12th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, December 4-7, 2013, Miami, FL, USA.
- Potentials of Nonlinear Dynamics Methods to Predict Customer Demands in Production Networks. In: - CIRP Sponsored Conference on Robust Manufacturing Control (RoMaC), CIRP, June 18-20, 2012, Bremen, Germany.
- Simulation-Based Generation of Time Series Representing Customer Demands in Networked Manufacturing Systems. 16th Annual International Conference on Industrial Engineering Theory, September 20-23, 2011, Stuttgart, Germany.
- Improved Forecasting Considering Dynamic Properties within the Time Series of Customer Demands. In: 11th WSEAS International Conference on Systems Theory and Scientific Computation (ISTASC '11), WSEAS, August 23-25, 2011, Florence, Italy.
- AdaptiveSBO - Ein adaptives simulationsbasiertes Optimierungsverfahren zur Planung und Steuerung dynamischer Produktionssysteme. BIBA-Forum, March 16, 2017, Bremen, Germany.
- Intelligente Selektion geeigneter Zeitreihenprognosemodelle durch Neuronale Netze. Workshop Prognose mit Künstlicher Intelligenz und Machine Learning, GOR, March 9-10, 2017, Hamburg, Germany.
- Meta-Lernen zur Modellselektion. Industry-Workshop, April 24, 2015, Hamburg, Germany.
- Automatische Prognose von Kundenbedarfen für die Produktionsplanung durch statistische Lernverfahren. Research-Workshop, January 22, 2015, Bremen, Germany.