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Extending simulation-based assembly planning to include human learning and previous experience: a simulation study

    Maximilian Duisberg Affiliation
    ; Michael Kranz   Affiliation
    ; Verena Nitsch Affiliation
    ; Susanne Mütze-Niewöhner Affiliation

Abstract

When using simulation-based assembly planning in the planning phase of designing modern assembly systems, the prospective system behavior should be predicted as reliably as possible by the simulation. For this purpose, personnel-related adjustment periods, such as those related to learning through task repetition should be considered in the simulation model, if employees are later to be involved in the assembly. The learning effect influences the overall performance of the system and can be described by learning curves. The aim of the approach presented in this paper is to increase the prediction quality of simulation models for assembly planning by taking into account the previous experience of the employees. For this purpose, a learning model is integrated into a discrete-event simulation and subsequently verified. The learning model includes the personnel-related learning curve as well as the previous experience of the employees as dynamic parameters. Simulation experiments with three forms of assembly organization were conducted to investigate the influence of learning and previous experience on the dynamic system behavior of an assembly system. The results indicate that assembly systems organized according to the One Piece Flow principle allow for broader, albeit slower, learning compared to row and group assembly.


First published online 18 January 2024

Keyword : learning models, learning curve, assembly, discrete event simulation, human factors, simulation, industrial engineering

How to Cite
Duisberg, M., Kranz, M., Nitsch, V., & Mütze-Niewöhner, S. (2023). Extending simulation-based assembly planning to include human learning and previous experience: a simulation study. New Trends in Computer Sciences, 1(2), 126–142. https://doi.org/10.3846/ntcs.2023.19040
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Dec 29, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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