Marvin May, M.Sc. M.Sc.

Marvin May, M.Sc. M.Sc.

Area of Research:

  • Machine Learning in Production Planning and Control
  • Control of flexible Production Systems
  • Digital Twin and Matrix Production
  • Big Data Analysis in Production
  • Sim2Real: fitting simulations to real behavior
  • Knowledge Graphs in Production
  • Industry 4.0

General Tasks:

  • Coordination of lecture…
    • Production Operations Management
    • Data Mining in Production (Seminar)
    • Process Mining in Production (Seminar)
  • Learning factory on global production scalable automatization and lean & industry4.0

Projects:

  • SDM4FZI
  • EU Digiman4.0 
  • Innovation Center SAP – Development of an AI-based multi-agent production control for matrix production
  • BaWue Robust

Curriculum Vitae:

since 09/2019

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

2013-2019

Industrial Engineering and Management student at KIT, graduated with M.Sc. & B.Sc.

2019

Exchange Student at Shanghai Jiaotong University (上海交通大学)

2017-2018

Exchange student & Research/Teaching Associate at the University of Massachusetts, Amherst and Isenberg School of Management

2017

Exchange student at Université de Strasbourg

2016

Exchange student at Beijing Institute of Technology (北京理工大学)

12/1994

Born

Publications


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
Optimierung einer Materialflusssteuerung zur Energieeffizienzerhöhung in der Produktion
Brützel, O.; Thiery, D.; May, M.; Lanza, G.
2022. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 117 (9), 591–596. doi:10.1515/zwf-2022-1106
Opportunistic maintenance scheduling with deep reinforcement learning
Valet, A.; Altenmüller, T.; Waschneck, B.; May, M. C.; Kuhnle, A.; Lanza, G.
2022. Journal of Manufacturing Systems, 64, 518–534. doi:10.1016/j.jmsy.2022.07.016
Modular, Digital Shopfloor Management Model – A Maturity Assessment For A Human-Oriented Transformation Process
Kandler, M.; Gabriel, P.; Schröttle, V.; May, M. C.; Lanza, G.
2022. Proceedings CPSL 2022 : Proceedings of the Conference on Production Systems and Logistics : CPSL 2022. Ed.: D. Herberger, 642–651, publish-Ing. doi:10.15488/12153
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
Towards narrowing the reality gap in electromechanical systems: error modeling in virtual commissioning
Kuhn, A. M.; May, M. C.; Liu, Y.; Kuhnle, A.; Tekouo, W.; Lanza, G.
2022. Production Engineering. doi:10.1007/s11740-022-01160-9
Hybrid Monte Carlo tree search based multi-objective scheduling
Hofmann, C.; Liu, X.; May, M.; Lanza, G.
2022. Production Engineering. doi:10.1007/s11740-022-01152-9
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
AI based geometric similarity search supporting component reuse in engineering design
Krahe, C.; Marinov, M.; Schmutz, T.; Hermann, Y.; Bonny, M.; May, M.; Lanza, G.
2022. 32nd CIRP Design Conference (CIRP Design 2022) - Design in a changing world. Ed.: N. Anwer, 275–280, Elsevier. doi:10.1016/j.procir.2022.05.249
Ontology-Based Production Simulation with OntologySim
May, M. C.; Kiefer, L.; Kuhnle, A.; Lanza, G.
2022. Applied Sciences (Switzerland), 12 (3), Art.-Nr.: 1608. doi:10.3390/app12031608
Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning
Wurster, M.; Michel, M.; May, M. C.; Kuhnle, A.; Stricker, N.; Lanza, G.
2022. Journal of Intelligent Manufacturing, 33 (2), 575–591. doi:10.1007/s10845-021-01863-3
Development of a Human-Centered Implementation Strategy for Industry 4.0 Exemplified by Digital Shopfloor Management
Kandler, M.; May, M. C.; Kurtz, J.; Kuhnle, A.; Lanza, G.
2022. Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021. Ed.: A.-L. Andersen, 738–745, Springer. doi:10.1007/978-3-030-90700-6_84
Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems
May, M. C.; Kiefer, L.; Kuhnle, A.; Stricker, N.; Lanza, G.
2021. Procedia CIRP, 96, 3–8. doi:10.1016/j.procir.2021.01.043
Leitfaden Antriebstechnik 4.0: Digitalisierungstrends für Produkt, Produktion und Lieferkette
Fleischer, J.; Lanza, G.; Wirth, F.; Gönnheimer, P.; Peukert, S.; May, M.; Hausmann, L.; Fraider, F.; Netzer, M.; Oexle, F.; Silbernagel, R.; Overbeck, L.
2021. VDMA Antriebstechnik
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
Multi-variate time-series for time constraint adherence prediction in complex job shops
May, M. C.; Behnen, L.; Holzer, A.; Kuhnle, A.; Lanza, G.
2021. 9th CIRP Global Web Conference – Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19. Ed.: K. Medini, 55–60, Elsevier. doi:10.1016/j.procir.2021.10.008
Reinforcement Learning Based Production Control of Semi-automated Manufacturing Systems
Overbeck, L.; Hugues, A.; May, M. C.; Kuhnle, A.; Lanza, G.
2021. 9th CIRP Global Web Conference – Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19. Ed.: K. Medini, 170–175, Elsevier. doi:10.1016/j.procir.2021.10.027
Foresighted digital twin for situational agent selection in production control
May, M. C.; Overbeck, L.; Wurster, M.; Kuhnle, A.; Lanza, G.
2021. Procedia CIRP, 99, 27–32. doi:10.1016/j.procir.2021.03.005
Data analytics for time constraint adherence prediction in a semiconductor manufacturing use-case
May, M. C.; Maucher, S.; Holzer, A.; Kuhnle, A.; Lanza, G.
2021. Procedia CIRP, 100, 49–54. doi:10.1016/j.procir.2021.05.008
Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems
Kapp, V.; May, M. C.; Lanza, G.; Wuest, T.
2020. Journal of Manufacturing and Materials Processing, 4 (3), 88. doi:10.3390/jmmp4030088
Metal additive manufacturing of multi-material dental strut implants
Kain, M.; Nadimpalli, V. K.; Miqueo, A.; May, M. C.; Yagüe-Fabra, J. A.; Häfner, B.; Pedersen, D. B.; Calaon, M.; Tosello, G.
2020. Proceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020. Ed.: R. K. Leach, 175–176, European Society for Precision Engineering and Nanotechnology (euspen)