Dr.-Ing. Marvin May

Dr.-Ing. Marvin May

Forschungs- und Arbeitsgebiete:

  • Maschinelles Lernen und KI in der Produktion

  • Semiconductor Manufacturing

  • Steuerung flexibler Produktionssysteme

  • Intelligente Produktionsplanung

  • Digitaler Zwilling, Industrial Internet of Things und Simulationen

  • Software-Defined Manufacturing und resiliente Produktion

  • Knowledge Engineering und Large Language Models (LLMs)

  • Gestaltung zirkulärer Produktion

  • Produkt-Produktions-CoDesign

  • Industrie 4.0 und Digitalisierungsstrategie im Produktionsnetzwerk

     

Allgemeine Aufgaben:

  • PostDoc Produktionssysteme
  • CIRP Research Affiliate
  • Koordination des Forschungsschwerpunkts Industrie 4.0
  • Vorlesungsbetreuer der Vorlesungen… 
    • Betriebliche Produktionswirtschaft
    • Data Mining in der Produktion, Process Mining in der Produktion (Seminare)
    • Integrierte Produktionsplanung (IPP)
    • SmartFactory@Industry
  • Lernfabrik für Globale Produktion

Dissertation:

Intelligent production control for time-constrained complex job shops - doi:10.5445/IR/1000169066

Projekte:

  • BMWi SDM4FZI – Software-Defined Manufacturing für die Fahrzeug- und Zulieferindustrie

  • EU MCSA Digiman4.0 - DIGItal MANufacturing Technologies for Zero-defect Industry 4.0 Production is a world excellent research training to 15 ESRs

  • Innovation Center SAP – Entwicklung einer lernenden Multi-Agenten-Steuerung für den Materialfluss in der Matrixproduktion

  • BaWü Robust - Assistenzsystem zur regelbasierten Robustheitssteigerung von verketteten Produktionssystemen

  • DFG SFB 1574 - Kreislauffabrik

  • DFG SPP 2187 - Toleranzfreie Serienfertigung von Hochleistungsbetonbauteilen durch transient-interaktive Kopplung von Entwurf und Produktion

  • BMWK CliCE-DiPP – Climate-neutral economy enabled by Digital Product Carbon Passport

  • ICM SDSeq - Software-Defined Model and Function Sequencing for Integrated Launching of Modular Production Systems

  • ICM GAIA-X4ICM - Infrastruktur für eine durchgängige Digitalisierung der Produktion auf Basis von Gaia-X

  • BMBF KARL - Kompetenzzentrum KARL: Künstliche Intelligenz für Arbeit und Lernen in der Region Karlsruhe

  • BMBF ChampI4.0ns - Intelligente und souveräne Nutzung von Daten am Beispiel der Holzindustrie

  • Innovation Center Bosch – Agile Production System

  • AgiloBat - Agile Produktion von Batteriezellen

  • DiKliMa - Digitalisierung, Klimamanagement & Mitarbeitergewinnung und -bindung

  • BMBF MoSyS - Menschorientierte Gestaltung komplexer System of Systems

  • BMBF teamIn - Digitale Führung und Technologien für die Teaminteraktion von morgen

  • EU Digiprime – Digital Platform for Circular Economy

     

Lebenslauf:

07/2024

Auszeichnung der Dissertation mit dem Dr.-Ing. Willy Höfler Preis der Fakultät für Maschinenbau (KIT)

2024

BMBF Exist academic supervisor puerro

2020-2024

Academic supervisor enactus e.V. Karlsruhe

seit 2024

 PostDoc

seit 09/2021

Oberingenieur und Gruppenleitung des Teams „Produktionssystemplanung“
09/2022-11/2022 Visiting Researcher National University of Singapore (NUS)
09/2019-08/2021 Wissenschaftlicher Mitarbeiter am Institut für Produktionstechnik (wbk) des Karlsruher Instituts für Technologie (KIT)

10/2016-08/2019

Studium des Wirtschaftsingenieurwesens (M.Sc.) und Wirtschaftsinformatik (M.Sc.) am Karlsruher Institut für Technologie (KIT)
02/2019-07/2019 Studium des International Business an der Shanghai Jiaotong University (上海交通大学)

09/2017-09/2018

Studium des Mechanical & Industrial Engineering und Graduate Teaching Assistant in Resource Economics an der University of Massachusetts, Amherst (UMass)
10/2013-09/2016 Studium des Wirtschaftsingenieurwesens (B.Sc.) am Karlsruher Institut für Technologie (KIT)

Veröffentlichungen


Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing
May, M. C.; Oberst, J.; Lanza, G.
2024. Journal of Intelligent Manufacturing, 35 (8), 4259–4276. doi:10.1007/s10845-024-02472-6
Reinforcement learning for sustainability enhancement of production lines
Loffredo, A.; May, M. C.; Matta, A.; Lanza, G.
2024. Journal of Intelligent Manufacturing, 35 (8), 3775–3791. doi:10.1007/s10845-023-02258-2
View planning in the visual inspection for remanufacturing using supervised- and reinforcement learning approaches
Kaiser, J.-P.; Koch, D.; Gäbele, J.; May, M. C.; Lanza, G.
2024. CIRP Journal of Manufacturing Science and Technology, 53, 128–138. doi:10.1016/j.cirpj.2024.07.006
Self-learning and autonomously adapting manufacturing equipment for the circular factory
Fleischer, J.; Zanger, F.; Schulze, V.; Neumann, G.; Stricker, N.; Furmans, K.; Pfrommer, J.; Lanza, G.; Hansjosten, M.; Fischmann, P.; Dvorak, J.; Klein, J.-F.; Rauscher, F.; Ebner, A.; May, M. C.; Gönnheimer, P.
2024. at - Automatisierungstechnik, 72 (9), 861–874. doi:10.1515/auto-2024-0005
Circular Production in Learning Factories: A Teaching Concept
Dvorak, J.; Hörsting, R.; Gleich, K.; Litterst, J.; May, M. C.; Lanza, G.
2024. Learning Factories of the Future ; Proceedings of the 14th Conference on Learning Factories 2024, Volume 1, Ed.: S. Thiede, 358 – 365, Springer Nature Switzerland. doi:10.1007/978-3-031-65411-4_42
Multi-Objective Mathematical Optimization in Assisted Production Planning
Schäfer, L.; Tse, S.; May, M. C.; Lanza, G.
2024. EurOMA - Transforming People and Processes for a better World, Barcelona, 29.06.-04.07.2024
Unterstützung von Mitarbeitenden in Montagelinien Gezielter Einsatz und Verknüpfung von Sensorik [Support of Employees in Assembly Lines Targeted Use and Linking of Sensor Technology]
Dvorak, J.; Amma, C.; Kandler, M.; Clever, F.; May, M. C.; Lanza, G.
2024. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 119 (6), 470 – 473. doi:10.1515/zwf-2024-1080
Intelligent production control for time-constrained complex job shops. Dissertation
May, M. C.
2024, März 27. Shaker. doi:10.5445/IR/1000169066
IIoT System Canvas — From architecture patterns towards an IIoT development framework
May, M. C.; Glatter, D.; Arnold, D.; Pfeffer, D.; Lanza, G.
2024. Journal of Manufacturing Systems, 72, 437 – 459. doi:10.1016/j.jmsy.2023.12.001
Automated model generation framework for material flow simulations of production systems
May, M. C.; Nestroy, C.; Overbeck, L.; Lanza, G.
2024. International Journal of Production Research, 62 (1-2), 141–156. doi:10.1080/00207543.2023.2284833
Framework for automatic production simulation tuning with machine learning
May, M. C.; Finke, A.; Theuner, K.; Lanza, G.
2024. Procedia CIRP, 121, 49 – 54. doi:10.1016/j.procir.2023.11.002
Selective disassembly planning considering process capability and component quality utilizing reinforcement learning
Tabar, R. S.; Magnanini, M. C.; Stamer, F.; May, M. C.; Lanza, G.; Wärmefjord, K.; Söderberg, R.
2024. Procedia CIRP, 121, 1 – 6. doi:10.1016/j.procir.2023.09.221
Towards a Service-Oriented Architecture for Production Planning and Control: A Comprehensive Review and Novel Approach
Behrendt, S.; Stamer, F.; May, M. C.; Lanza, G.
2023. Proceedings of the 5th Conference on Production Systems and Logistics (CPSL-2). Ed.: D. Herberger, 255–267, publish-Ing. doi:10.15488/15271
Interoperable Architecture For Logical Reconfigurations Of Modular Production Systems
Behrendt, S.; Wurster, M.; Strljic, M.; Klein, J.-F.; May, M. C.; Lanza, G.
2023. Proceedings of the 5th Conference on Production Systems and Logistics (CPSL-2). Ed.: D. Herberger, 622–633, publish-Ing. doi:10.15488/15270
Reinforcement learning for energy-efficient control of multi-stage production lines with parallel machine workstations
Loffredo, A.; May, M. C.; Matta, A.
2023. Italian Manufacturing Association Conference, 428 – 436, Materials Research Forum LLC. doi:10.21741/9781644902714-51
Improving Production System Flexibility and Changeability Through Software-Defined Manufacturing
Behrendt, S.; Ungen, M.; Fisel, J.; Hung, K.-C.; May, M.-C.; Leberle, U.; Lanza, G.
2023. A. B. T. M. H.-C. Liewald Mathiasand Verl (Hrsg.), Production at the Leading Edge of Technology – Proceedings of the 12th Congress of the German Academic Association for Production Technology (WGP), University of Stuttgart, October 2022. Ed.: M. Liewald, 705–716, Springer International Publishing. doi:10.1007/978-3-031-18318-8_70
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
Generalization of Reinforcement Learning Agents for Production Control
Overbeck, L.; Glaser, V.; May, M. C.; Lanza, G.
2023. Production Processes and Product Evolution in the Age of Disruption. Ed.: F. Galizia, 338–346, Springer International Publishing. doi:10.1007/978-3-031-34821-1_37
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
A development approach for a standardized quality data model using asset administration shell technology in the context of autonomous quality control loops for manufacturing processes
Bilen, A.; Stamer, F.; May, M. C.; Lanza, G.
2023. Conference Proceedings : 23rd International Conference & Exhibition, 1–4, Euspen
Software-Defined Manufacturing for the Entire Life Cycle at Different Levels of Production
Behrendt, S.; Martin, M.; Puchta, A.; Ströbel, R.; Fisel, J.; May, M.; Gönnheimer, P.; Fleischer, J.; Lanza, G.
2023. Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains : Stuttgart Conference on Automotive Production (SCAP2022). Ed.: N. Kiefl, 25–34, Springer International Publishing. doi:10.1007/978-3-031-27933-1_3
Extended Production Planning of Reconfigurable Manufacturing Systems by Means of Simulation-based Optimization
Behrendt, S.; Wurster, M.; May, M. C.; Lanza, G.
2023. 4th Conference on Production Systems and Logistics (CPSL 2023), 210–220, publish-Ing. doi:10.15488/13440
Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’
Chen, T.; Sampath, V.; May, M. C.; Shan, S.; Jorg, O. J.; Aguilar Martín, J. J.; Stamer, F.; Fantoni, G.; Tosello, G.; Calaon, M.
2023. Applied Sciences (Switzerland), 13 (3), Art.-Nr.: 1903. doi:10.3390/app13031903
A Systematic Approach to Task Assignment and Production Planning in Disassembly with Employee Skills
Dvorak, J.; Wurster, M.; May, M. C.; Lanza, G.
2023. Procedia CIRP, 120, 958–963. doi:10.1016/j.procir.2023.09.107
Applying frequency based forecasting for resource allocation
May, M. C.; Kiefer, L.; Frey, A.; Duffie, N. A.; Lanza, G.
2023. Procedia CIRP, 120, 147 – 152. doi:10.1016/j.procir.2023.08.027
Human-centered Digital Shopfloor Management Implementation and Acceptance Model
Kandler, M.; Seibert, C.; May, M. C.; Lanza, G.
2023. Procedia CIRP, 120, 1321 – 1326. doi:10.1016/j.procir.2023.09.170
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
Artificial Intelligence Implementation Strategy for Industrial Companies Using the AI Tool Box - A Morphology for Selecting Relevant AI Use Cases
Beiner, S.; Kandler, M.; May, M. C.; Kinkel, S.; Lanza, G.; Richter, D.
2023. Production Processes and Product Evolution in the Age of Disruption – Proceedings of the 9th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2023) and the 11th World Mass Customization & Personalization Conference (MCPC2023), Bologna, Italy, June 2023, 763 – 773, Springer International Publishing. doi:10.1007/978-3-031-34821-1_83
Assessment of the potential of gamification in manual assembly
Dvorak, J.; Merforth, M.; Kandler, M.; Clever, F.; May, M. C.; Lanza, G.
2023. 13th Conference on Learning Factories (CLF 2023) Hrsg.: Hummel, Vera. doi:10.2139/ssrn.4471436
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
Creation and validation of systems for product and process configuration based on data analysis
Frey, A. M.; May, M. C.; Lanza, G.
2023. Production Engineering, 12 (2), 263–277. doi:10.1007/s11740-022-01176-1
Graph-based prediction of missing KPIs through optimization and random forests for KPI systems
May, M. C.; Fang, Z.; Eitel, M. B. M.; Stricker, N.; Ghoshdastidar, D.; Lanza, G.
2023. Production Engineering, 17 (2), 211–222. doi:10.1007/s11740-022-01179-y
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
Nutzungskonzept für Digitale Zwillinge von Produktionssystemen : Integration in die Organisation und Prozesse der Produktionsplanung = Utilization Concept for Digital Twins of Production Systems : Integration into the Organization and Production Planning Processes
Overbeck, L.; Rose, A.; May, M.; Lanza, G.
2022. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 117 (4), 244–248. doi:10.1515/zwf-2022-1035
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, 17 (3-4), 739–752. 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, 17, 133–144. 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
Product Generation Module: Automated Production Planning for optimized workload and increased efficiency in Matrix Production Systems
May, M. C.; Schmidt, S.; Kuhnle, A.; Stricker, N.; Lanza, G.
2021. Procedia CIRP, 96, 45–50. doi:10.1016/j.procir.2021.01.050
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)