Marco Wurster, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Marco Wurster, M.Sc.

Area of Research:

  • Machine Learning for production planning and control
  • Scalable automation of agile production systems
  • Plug&Produce
  • Remanufacturing at factory level
  • Factory planning

General Tasks:

  • Coordination of lecture "Integrated production planning in the age of Industry 4.0" (IPP)
    • Assembly planning: concept and detailed planning
  • Coordinator Learning Factory Module Scalable Automation
  • Representative in the IHK Karlsruhe "Arbeitskreis Industrie 4.0"


Test benches:

Curriculum Vitae:

sincet 10/2019 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT) 
10/2012-06/2019  Study of Mechanical Engineering at Karlsruhe Institute of Technology (KIT)
09/04/1993 Born in Breisach am Rhein



[ 1 ] May, M. C.; Overbeck, L.; Wurster, M.; Kuhnle, A. & Lanza, G. (2020), "Foresighted Digital Twin for situational Agent Selection in Production Control". Elsevier, pp. 27-32. 10.1016/j.procir.2021.03.005
As intelligent Data Acquisition and Analysis in Manufacturing nears its apex, a new era of Digital Twins is dawning. Foresighted Digital Twins enable short- to medium-term system behavior predictions to infer optimal production operation strategies. Creating up-to-the-minute Digital Twins requires both the availability of real-time data and its incorporation and serve as a stepping-stone into developing unprecedented forms of production control. Consequently, we regard a new concept of Digital Twins that includes foresight, thereby enabling situational selection of production control agents. One critical element for adequate system predictions is human behavior as it is neither rule-based nor deterministic, which we therefore model applying Reinforcement Learning. Owing to these ever-changing circumstances, rigid operation strategies crucially restrain reactions, as opposed to circumstantial control strategies that hence can outperform traditional approaches. Building on enhanced foresights we show the superiority of this approach and present strategies for improved situational agent selection.

[ 2 ] Wurster, M.; Exner, Y.; Kaiser, J.; Stricker, N. & Lanza, G. (2021), "Towards planning and control in cognitive factories - A generic model including learning effects and knowledge transfer across system entities". Procedia CIRP, Elsevier, Amsterdam, pp. 158-163. 10.1016/j.procir.2021.10.025
Cognitive abilities allow robots to learn and reason from their environment. The gained knowledge can then be incorporated into the robot’s actions which in turn affect the environment. Therefore, a cognitive robot is no longer a static system that performs actions based on a pre-defined set of rules but a complex entity that dynamically adjusts over time. With this, challenges arise for production systems that need to observe and ideally anticipate the cognitive robot’s behavior. Often, digital twins are employed to test and optimize production control systems. This paper presents a generic approach to characterize, model and simulate learning processes and formalized knowledge in hybrid production systems assuming different station types with learning effects. Thereby, quantitative and qualitative learning processes are mapped including knowledge sharing and transfer across entities. A modular and parameterizable design enables the adjustment to different use cases. Eventually, the model is instantiated as a digital twin of a real production system for product disassembly employing cognitive-autonomous robots among human operators and rigidly automated machines. The model shows great potential to be integrated into test beds for planning and control systems of cognitive factories.

[ 3 ] Wurster, M.; Häfner, B.; Gauder, D.; Stricker, N. & Lanza, G. (2021), "Fluid Automation - A Definition and an Application in Remanufacturing Production Systems". Digitalizing smart factories, Elsevier, pp. 508-513. 10.1016/j.procir.2020.05.267
Production systems must be able to quickly adapt to changing requirements. Especially in the field of remanufacturing, the uncertainty in the state of the incoming products is very high. Several adaptation mechanisms can be applied leading to agile and changeable production systems. Among these, adapting the degree of automation with respect to changeover times and high investment costs is one of the most challenging mechanisms. However, not only long-term changes, but also short-term adaptations can lead to enormous potentials, e.g. when night shifts can be supported by robots and thus higher labor costs and unfavorable working conditions at night can be avoided. These changes in the degree of automation on an operational level are referred to as fluid automation, which will be defined in this paper. The mechanisms of fluid automation are presented together with a case study showing its application on a disassembly station for electrical drives.

[ 4 ] Sprenger, K.; Klein, J.; Wurster, M.; Stricker, N. & Lanza, G. (2021), "Industrie 4.0 im Remanufacturing", Industrie 4.0 Management, vol. 37, no. 4, pp. 37-40. 10.30844/I40M_21-4_S37-40
Das Remanufacturing, bisher geprägt durch manuelle und kostenintensive Prozesse, ist ein entscheidender Schritt auf dem Weg zu einer ressourcenschonenden Kreislaufwirtschaft. Industrie und Forschung sind sich einig, dass der Einzug von Industrie 4.0 Technologien den Schlüssel zu einer Entwicklung automatisierter und wirtschaftlicher Remanufacturing-Systeme darstellt. Basierend auf einer systematischen Literaturrecherche widmet sich dieser Beitrag der Analyse vielversprechender Industrie 4.0-Ansätze mit dem Fokus auf den übergeordneten Gesamtprozess sowie den Teilprozessen der Demontage und der Inspektion. Die Ergebnisse legen nahe, dass es an zusätzlichem Wissen, Erfahrung und Forschung bei der Entwicklung und realen Demonstration der Ansätze und deren Übertragbarkeit auf breitere Anwendungsfelder bedarf.

[ 5 ] Klein, J.; Wurster, M.; Stricker, N.; Lanza, G. & Furmans, K. (2021), "Towards Ontology-based Autonomous Intralogisticsfor Agile Remanufacturing Production Systems". IEEE, 10.1109/ETFA45728.2021.9613486
Remanufacturing, previously characterised by manual and cost-intensive processes, is a decisive step towards a resource-conserving circular economy. Uncertain product states, inconsistent quality, and fluctuating availability of end-of-life products not only pose major challenges for the automation of remanufacturing, but also for intralogistics, which has hardly been considered in the literature to date. This paper gives a concept overview on an ontology-based autonomous intralogistics system embedded in the fluid automation framework, describes and illustrates the main cyber-physical components and shows exemplary workflows. The presented concept is currently implemented as part of the AgiProbot research project.

[ 6 ] Kaiser, J.; Becker, S. N.; Wurster, M.; Stricker, N. & Lanza, G. (2021), "Framework for simulation-based Trajectory Planning and Execution of Robots equipped with a Laser Scanner for Measurement and Inspection". 9th CIRP Global Web Conference – Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19, pp. 292-297. DOI: 10.1016/j.procir.2021.10.047

[ 7 ] Wurster, M.; Michel, M.; May, M. C.; Kuhnle, A.; Stricker, N. & Lanza, G. (2022), "Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning", Journal of Intelligent Manufacturing, no. 2, pp. 575–591. 10.1007/s10845-021-01863-3
Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases.