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Leonard Overbeck, M.Sc.

Research Associate
department: Production Systems
office hours: to be agreed
room: 109, Geb. 50.36
phone: +49 1523 9502641
Leonard OverbeckKpa4∂kit edu

76131 Karlsruhe
Kaiserstraße 12

Leonard Overbeck, M.Sc.

Area of Research: 

  • Artificial Intelligence in Production Planning and Control
  • Production control of flexible production systems
  • Development and introduction of Industry 4.0 applications


General Tasks:

  • Coordination of lectur integrated production planning in the age of I4.0 (IPP)
    • Principles of Lean Production
    • Detailed plant layout



  • Bosch Innovation Center Agile Production System



since 05/2019 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)
10/2011-04/2018 Study of Industrial Engineering at Karlsruhe Institute of Technology (KIT)



[ 1 ] Greinacher, S.; Overbeck, L.; Kuhnle, A.; Krahe, C. & Lanza, G. (2020), "Multi-objective optimization of lean and resource efficient manufacturing systems", Production Engineering, pp. 1-12. https://doi.org/10.1007/s11740-019-00945-9
In the manufacturing industry, target-oriented and efficient use of resources is gaining importance, alongside economic optimization. The economic and organizational optimization of manufacturing systems according to the lean principles is only partly compatible with the goals of resource-efficient manufacturing. Therefore, an approach is sought to improve individual analyses of manufacturing systems. This paper proposes an approach for the multi-objective optimization of lean and resource-efficient manufacturing systems. To predict the dynamic effects of several configurations of manufacturing systems, material, energy, and information flows of a discrete event simulation are coupled with an assessment model, based on objectives of lean and resource-efficient manufacturing. Using design of experiments, Gaussian process meta-models are computed for the behavior of the simulation model. These meta-models allow the approximation of the system behavior to be computed in a short period of time and enable extensive multi-objective optimization and more adequate decision-making support systems. The proposed approach is tested in the metalworking industry.