Louis Schäfer, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Louis Schäfer, M.Sc

Area of Research:

  • Product-Production-Codesign
  • Machine Learning in Production Planning and Control
  • Industrie 4.0

General Tasks:


  • BMBF MoSyS - Human-oriented design of complex System of Systems
  • BMBF teamIn - Digital leadership and technologies for the team interaction of tomorrow
  • IFREE Grant H2R - Human & Robot Interaction


Curriculum Vitae:

since 10/2020

Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)


Study of Mechanical Engineering at Karlsruhe Institute of Technology (KIT)


A systematic approach for simulation-based dimensioning of production systems during the concept phase of factory planning
Schäfer, L.; Klenk, F.; Maier, T.; Zehner, M.; Peukert, S.; Linzbach, R.; Treiber, T.; Lanza, G.
2024. Production Engineering. doi:10.1007/s11740-024-01273-3
Reinforcement learning for energy-efficient control of parallel and identical machines
Loffredo, A.; May, M. C.; Schäfer, L.; Matta, A.; Lanza, G.
2023. CIRP Journal of Manufacturing Science and Technology, 44, 91–103. doi:10.1016/j.cirpj.2023.05.007
Reinforcement Learning for Improvement Measure Selection in Learning Factories
May, M. C.; Hermeler, S.; Mauch, E.; Dvorak, J.; Schäfer, L.; Lanza, G.
2023. Proceedings of the 13th Conference on Learning Factories (CLF 2023). Hrsg.: Hummel, Vera, Social Science Electronic Publishing. doi:10.2139/ssrn.4470426
Planning and Multi-Objective Optimization of Production Systems by means of Assembly Line Balancing
Schäfer, L.; Kochendörfer, P.; May, M. C.; Lanza, G.
2023. Procedia CIRP, 120, 1125 – 1130. doi:10.1016/j.procir.2023.09.136
Systematics for an Integrative Modelling of Product and Production System
Schäfer, L.; Günther, M.; Martin, A.; Lüpfert, M.; Mandel, C.; Rapp, S.; Lanza, G.; Anacker, H.; Albers, A.; Köchling, D.
2023. 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2023), 118, 104–109. doi:10.1016/j.procir.2023.06.019
Towards Product-Production-CoDesign for the Production of the Future
May, M. C.; Schäfer, L.; Frey, A.; Krahe, C.; Lanza, G.
2023. Procedia CIRP, 119, 944–949. doi:10.1016/j.procir.2023.02.172
Classifying Parts using Feature Extraction and Similarity Assessment
Schäfer, L.; Treml, N.; May, M. C.; Lanza, G.
2023. Procedia CIRP, 119, 822–827. doi:10.1016/j.procir.2023.03.127
Explainable reinforcement learning in production control of job shop manufacturing system
Kuhnle, A.; May, M. C.; Schäfer, L.; Lanza, G.
2022. International Journal of Production Research, 60 (19), 5812–5834. doi:10.1080/00207543.2021.1972179
Produkt-Produktions-CoDesign: Ein Ansatz zur integrierten Produkt- und Produktionssystementwickung über Generationen und Lebenszyklen hinweg
Albers, A.; Rapp, S.; Klippert, M.; Lanza, G.; Schäfer, L.
2022. News / Wissenschaftliche Gesellschaft für Produktentwicklung, WiGeP, Berliner Kreis & WGMK, (1), 3
New Competences in a Digitalized Shopfloor – A Modular Training Concept for Learning Factories
Schäfer, L.; Ströhlein, K.; Kandler, M.; Hulla, M.; Ast, J.; Lanza, G.; Nieken, P.; Ramsauer, C.; Nyhuis, P.
2022. SSRN eLibrary, Art.-Nr. 4071822. doi:10.2139/ssrn.4071822
Decision Experiments in the Learning Factory: A Proof of Concept
Ströhlein, K.; Gorny, P. M.; Kandler, M.; Schäfer, L.; Nieken, P.; Lanza, G.
2022. Proceedings of the 12th Conference on Learning Factories (CLF 2022), Art.-Nr. 4072356, SSRN. doi:10.2139/ssrn.4072356
Applying Natural Language Processing in Manufacturing
May, M. C.; Neidhöfer, J.; Körner, T.; Schäfer, L.; Lanza, G.
2022. Procedia CIRP, 10th CIRP Global Web Conference – Material Aspects of Manufacturing Processes, 115, 184–189. doi:10.1016/j.procir.2022.10.071
Shopfloor Management Acceptance in Global Manufacturing
Kandler, M.; Dierolf, L.; Bender, M.; Schäfer, L.; May, M. C.; Lanza, G.
2022. Procedia CIRP, 10th CIRP Global Web Conference – Material Aspects of Manufacturing Processes, 115, 190–195. doi:10.1016/j.procir.2022.10.072
Digitale Transformation in global produzierenden Unternehmen = Digital Transformation in global manufacturing companies
Steier, G.; Schäfer, L.; Moser, S.; Kandler, M.; Lanza, G.
2022. WT Werkstattstechnik, 112 (5), 314–319. doi:10.37544/1436–4980–2022–5–44
KI-Assistenzsysteme in der Produktentwicklung
Schäfer, L.; Krahe, C.
2022. Potentiale digitaler Führung und Technologien für die Teaminteraktion von morgen : Zwischenbericht des vom BMBF geförderten Forschungs- und Entwicklungsprojektes im Rahmen des Programms. Hrsg.: G. Lanza, 68 – 74, TEWISS Verl
Automated Derivation of Optimal Production Sequences from Product Data
Schäfer, L.; Frank, A.; May, M. C.; Lanza, G.
2022. Procedia CIRP, 107, 469–474. doi:10.1016/j.procir.2022.05.010
Product-Production-CoDesign: An Approach on Integrated Product and Production Engineering Across Generations and Life Cycles
Albers, A.; Lanza, G.; Klippert, M.; Schäfer, L.; Frey, A.; Hellweg, F.; Müller-Welt, P.; Schöck, M.; Krahe, C.; Nowoseltschenko, K.; Rapp, S.
2022. 32nd CIRP Design Conference (CIRP Design 2022) - Design in a changing world. Ed.: N. Anwer, 167–172, Elsevier. doi:10.1016/j.procir.2022.05.231
Learning Factory Labs as Field-in-the-Lab Environments – An Experimental Concept for Human-Centred Production Research
Kandler, M.; Schäfer, L.; Gorny, P. M.; Ströhlein, K.; Lanza, G.; Nieken, P.
2021. Proceedings of the 11th Conference on Learning Factories (CLF)
Towards a User Support System for Computed Tomography Measurements Using Machine Learning
Höger, K.; Schäfer, L.; Schild, L.; Lanza, G.
2021. Production at the Leading Edge of Technology: Proceedings of the 11th Congress of the German Academic Association for Production Technology (WGP), Dresden, September 2021. Edited by Bernd-Arno Behrens, Alexander Brosius, Welf-Guntram Drossel, Wolfgang Hintze, Steffen Ihlenfeldt, Peter Nyhuis, 506–514, Springer. doi:10.1007/978-3-030-78424-9_56
Queue Length Forecasting in Complex Manufacturing Job Shops
May, M. C.; Albers, A.; Fischer, M. D.; Mayerhofer, F.; Schäfer, L.; Lanza, G.
2021. Forecasting, 2021 (2), 322–338. doi:10.3390/forecast3020021