Publications

Contributions to Journals (Reviewed)

1. Quadras, D. L. O.; Cavalcante, I.; Kück, M.; Frazzon, E. M. (2023): Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process. In: Applied Sciences 13(19), 10904, p. 1-14.

2. Gumz, J.; Fettermann, D. C.; Frazzon, E. M.; Kück, M. (2022): Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions. In: Sustainability 14(20), 13642, p. 1-34.

3. Kück, M.; Freitag, M. (2021): Forecasting of customer demands for production planning by local k-nearest neighbor models. In: International Journal of Production Economics 237(107837), p. 1-22. 

4. Leusin, M.E.; Frazzon, E.M.; Uriona Maldonado, M.; Kück, M.; Freitag, M. (2018): Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era. In: Technologies 6(4), p. 1-27. 

5. Frazzon, E.M.; Kück, M.; Freitag, M. (2018): Data-driven production control for complex and dynamic manufacturing systems. In: CIRP Annals - Manufacturing Technology 67(1), p. 515-518.

6. Frazzon, E. M.; Albrecht, A.; Pires, M.; Israel, E.; Kück, M.; Freitag, M. (2018): Hybrid approach for the integrated scheduling of production and transport processes along supply chains. In: International Journal of Production Research 56(5), p. 2019-2035. 

7. Kück, M.; Ehm, J.; Hildebrandt, T.; Freitag, M.; Frazzon, E. M. (2017): Adaptive PPC by simulation-based optimization (German title: Adaptive PPS durch simulationsbasierte Optimierung - Selektion geeigneter Prioritätsregeln basierend auf Echtzeitinformationen einer Werkstattfertigung). In: wt Werkstattstechnik online 4/2017, p. 288-292.

8. Staar, B.; Kück, M.; Ait Alla, A.; Lütjen, M.; Simic. A.; Freitag, M. (2017): Anomaly Detection in Images of Micro Parts (German title: Statistische Detektion von Anomalien in Bilddaten von Mikrobauteilen). In: Industrie 4.0 Management 33(2), p. 52-56.

9. Kück, M.; Ehm, J.; Freitag, M.; Frazzon, E. M. (2016): Concept of an Adaptive Simulation-Based Optimization Method for the Scheduling and Control of Dynamic Manufacturing Systems (German title: Adaptives simulationsbasiertes Optimierungsverfahren - Konzept zur Planung und Steuerung dynamischer Produktionssysteme). In: Industrie 4.0 Management 32(5), p. 26-31. 

10. Becker, T.; Kück, M.; Hardemann, F. (2015): Opportunities and Risks of Shared Resources in Production Networks (German title: Chancen und Risiken von Shared Resources in Produktionsnetzwerken). In: Industrie 4.0 Management 31(4), p. 25-29. 

11. Freitag, M.; Kück, M.; Ait Alla, A.; Lütjen, M. (2015): Potentials of Data Science in Production and Logistics: Part 2 - Approach to Data Analysis and Application Examples (German title: Potenziale von Data Science in Produktion und Logistik: Teil 2 - Vorgehensweise zur Datenanalyse und Anwendungsbeispiele). In: Industrie 4.0 Management 31(6), p. 39-46. 

12. Freitag, M.; Kück, M.; Ait Alla, A.; Lütjen, M. (2015): Potentials of Data Science in Production and Logistics: Part 1 - An Introduction into Current Approaches of Data Science (German title: Potenziale von Data Science in Produktion und Logistik: Teil 1 - Eine Einführung in aktuelle Ansätze der Data Science). In: Industrie 4.0 Management 31(5), p. 22-26. 

13. Scholz-Reiter, B.; Kück, M.; Lappe, D. (2014): Prediction of customer demands for production planning - Automated selection and configuration of suitable prediction methods. In: CIRP Annals - Manufacturing Technology 63(1), p. 417-420.

14. Scholz-Reiter, B.; Kück, M. (2012): Selection of Forecasting Methods for Customer Demands - Development of a Data Base with Recommendations for Selecting Suitable Forecasting Methods (German title: Auswahl von Prognoseverfahren für Kundenbedarfe - Erstellung einer Datenbank mit Handlungsempfehlungen zur Auswahl geeigneter Prognoseverfahren). In: Industrie Management 28(1), p. 61-65.

Contributions to Conference Proceedings (Reviewed)

1. da Silva, M. R. F.; Kück, M.; Frazzon, E. M.; Bremen, J. C. (2024): Analysis of Machine Learning Approaches to Predict Disruptions in Truck Appointment Systems. In: Dynamics in Logistics - Proceedings of the 9th International Conference LDIC 2024, Bremen, Germany, p. 467-476. 

2. Leusin, M. E.; Kück, M.; Frazzon, E. M.; Uriona, M.; Freitag, M. (2018): Potential of a Multi-Agent System Approach for Production Control in Smart Factories. In: Proceedings of the 16th IFAC Symposium on Information Control Problems in Manufacturing (INCOM), IFAC, Bergamo, Italy, pp. 1459–1464.

3. Pires, M. C.; Frazzon, E. M.; Danielli, A. M. C.; Kück, M.; Freitag, M. (2018): Towards a simulation-based optimization approach to integrate supply chain planning and control. In: Proceedings of the 51st CIRP Conference on Manufacturing Systems, Stockholm, Sweden, p. 520-525.

4. Kück, M.; Broda, E.; Freitag, M.; Hildebrandt, T.; Frazzon, E. M. (2017): Towards Adaptive Simulation-Based Optimization to Select Individual Dispatching Rules for Production Control. In: Proceedings of the 2017 Winter Simulation Conference, IEEE, Las Vegas, NV, USA, p. 3852-3863.

5. Vieira, G. E.; Kück, M.; Frazzon, E. M.; Freitag, M. (2017): Evaluating the Robustness of Production Schedules using Discrete-Event Simulation. In: 20th IFAC World Congress Proceedings, IFAC, Toulouse, France,  p. 7953-7958.

6. Freitag, M.; Kück, M.; Becker, T. (2016): Potentials and Risks of Resource Sharing in Production and Logistics. In: Proceedings of the 8th International Scientific Symposium on Logistics. Logistics in the Times of the 4th Industrial Revolution - Ideas, Concepts, Scientific Basis, BVL, Bremen, Germany, p. 199-209.  

7. Kück, M.; Becker, T.; Freitag, M. (2016): Emergence of Non-predictable Dynamics Caused by Shared Resources in Production Networks. In: Procedia CIRP. Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems, Ischia (Naples), Italy, p. 520-525.

8. Kück, M.; Crone, S. F.; Freitag, M. (2016): Meta-Learning with Neural Networks and Landmarking for Forecasting Model Selection - An Empirical Evaluation of Different Feature Sets Applied to Industry Data. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, Vancouver, BC, Canada , p. 1499-1506.

9. Kück, M.; Ehm, J.; Hildebrandt, T.; Freitag, M.; Frazzon, E. M. (2016): Potential of Data-Driven Simulation-Based Optimization for Adaptive Scheduling and Control of Dynamic Manufacturing Systems. In: Proceedings of the 2016 Winter Simulation Conference, IEEE, Washington, D.C., USA, p. 2820-2831.

10. Kück, M.; Ehm, J.; Freitag, M.; Frazzon, Enzo M.; Pimentel, R. (2016): A Data-driven Simulation-based Optimisation Approach for Adaptive Scheduling and Control of Dynamic Manufacturing Systems. In: Proceedings of the WGP Congress 2016, Advanced Materials Research. Trans Tech Publications, WGP, Hamburg, Germany, p. 449-456.

11. Kück, M.; Scholz-Reiter, B.; Freitag, M. (2014): Robust Methods for the Prediction of Customer Demands Based on Nonlinear Dynamical Systems. In: Procedia CIRP. Proceedings of the 2nd CIRP Robust Manufacturing Conference (RoMac 2014), Bremen Germany, p. 93-98.

12. Thoben, K.-D.; Veigt, M.; Lappe, D.; Franke, M.; Kück, M.; [...]; Zimmerling, R.; Schlick, J.; Stephan, P.; Guth, P. (2014): Towards Networking Logistics Resources to enable a Demand-Driven Material Supply for Lean Production Systems - Basic Concept and Potential of a Cyber-Physical Logistics System. In: Proceedings of the 7. BVL Scientific Symposium on Logistics, BVL, Bremen, Germany, p. 42-69.

13. Kück, M.; Scholz-Reiter, B. (2013): A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands. In: Proceedings of the 12th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, Miami, FL, USA, p. 160-165.

14. Scholz-Reiter, B.; Kück, M. (2012): Potentials of Nonlinear Dynamics Methods to Predict Customer Demands in Production Networks. In: Robust Manufacturing Control - Proceedings of the CIRP Sponsored Conference RoMaC 2012, Lecture Notes in Production Engineering, CIRP, Bremen, Germany, p. 33-45.

Contributions to Journals (Non-Reviewed)

1. Scholz-Reiter, B.; Kück, M.; Toonen, C. (2012): Improved Demand Forecasting Using Local Models Based on Delay Time Embedding. In: International Journal of Systems Applications, Engineering & Development 6(1), p. 17-27.

Contributions to Conference Proceedings (Non-Reviewed)

1. Kück, M.; Scholz-Reiter, B. (2013): Forecasting of Customer Demands in Production Networks Based on Phase Space Reconstruction - An application to predict intermittent demand evolutions. In: Proceedings of the 33rd International Symposium on Forecasting, IIF, Seoul, South Korea, p. 6.

2. Scholz-Reiter, B.; Kück, M.; Toonen, C. (2011): Simulation-Based Generation of Time Series Representing Customer Demands in Networked Manufacturing Systems. In: Proceedings of the 16th Annual International Conference on Industrial Engineering Theory, Stuttgart, Germany, p. 488-493.

3. Scholz-Reiter, B.; Kück, M.; Toonen, C. (2011): Improved Forecasting Considering Dynamic Properties within the Time Series of Customer Demands. In: Recent Advances in Signal Processing, Computational Geometry and Systems Theory. Proceedings of the 11th International Conference on Systems Theory and Scientific Computation (ISTASC '11), Florence, Italy, p. 103-108.