Jens Heger
Prof. Dr.
- Engineering
Research areas
- Published
Dynamische Losgrößenoptimierung mit bestärkendem Lernen
Voß, T., Bode, C. & Heger, J., 2021, In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb. 116, 11, p. 815-819 5 p.Research output: Journal contributions › Journal articles › Research › peer-review
Dynamische Regelselektion in der Reihenfolgeplanung: Prognose von Steuerungsparametern mit Gaußschen Prozessen
Heger, J., 2014, Wiesbaden: Springer Verlag. 167 p. (Research)Research output: Books and anthologies › Monographs › Research › peer-review
- Published
Einsatz von bestärkendem Lernen in der Reihenfolgeplanung mit dem Ziel der platzeffizienten Produktion
Müller, K. & Heger, J., 2025, Simulation in Produktion und Logistik 2025. Rank, S., Kühn, M. & Schmidt, T. (eds.). Dresden: Dresden University of Technology, 10 p. 40. (ASIM-Mitteilung; no. 194)(Tagungsband ASIM-Fachtagung Simulation in Produktion und Logistik; vol. 21).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
- Published
Enabling Road Condition Monitoring with an on-board Vehicle Sensor Setup
Kortmann, F., Peitzmeier, H., Meier, N., Heger, J. & Drews, P., 10.2019, 2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings: Conference proceedings. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 4 p. 8956699. (Proceedings of IEEE Sensors; vol. 2019-October).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
- Published
Enhancing SME Production Efficiency: A Case Study on Autonomous Manufacturing Systems
Prüfer, O. C. & Heger, J., 2025, In: Procedia CIRP. 134, p. 313-318 6 p.Research output: Journal contributions › Conference article in journal › Research › peer-review
- Published
Entscheidungsbäume und bestärkendes Lernen zur dynamischen Auswahl von Reihenfolgeregeln in einem flexiblen Produktionssystem
Heger, J., Zein El Abdine, M., Sekar, S. & Voß, T., 26.08.2021, Simulation in Produktion und Logistik 2021: Erlangen, 15. - 17. September 2021. Franke, J. & Schuderer, P. (eds.). Göttingen: Cuvillier Verlag, p. 337-346 10 p. (ASIM-Mitteilung; vol. AM 177).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems
Pickardt, C. W., Hildebrandt, T., Branke, J., Heger, J. & Scholz-Reiter, B., 09.2013, In: International Journal of Production Economics. 145, 1, p. 67-77 11 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Failure prediction by using a recurrent neural network in incremental sheet forming with active medium
Thiery, S., Zein El Abdine, M., Heger, J. & Ben Khalifa, N., 30.10.2025, In: International Journal of Material Forming. 18, 4, 15 p., 91.Research output: Journal contributions › Journal articles › Research › peer-review
Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques
Scholz-Reiter, B., Heger, J. & Hildebrandt, T., 2010, Proceedings - IEEE International Conference on Data Mining, ICDM. IEEE - Institute of Electrical and Electronics Engineers Inc., p. 631-638 8 p. (IEEE International Conference on Data Mining Workshops).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness
Pickardt, C., Branke, J., Hildebrandt, T., Heger, J. & Scholz-Reiter, B., 2010, Proceedings - Winter Simulation Conference. IEEE - Institute of Electrical and Electronics Engineers Inc., p. 2504-2515 12 p.Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review