Extending simulation-based assembly planning to include human learning and previous experience: a simulation study
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
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Liebau, H. (2002). Die Lernkurvenmethode. Ergonomia, Stuttgart.
Nembhard, D. A. (2014). Cross training efficiency and flexibility with process change. International Journal of Operations & Production Management, 34(11), 1417–1439. https://doi.org/10.1108/IJOPM-06-2012-0197
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Wang, Q., & Abubakar, M. I. (2017). Human factors and their effects on human-centred assembly systems – A literature review-based study. IOP Conference Series: Materials Science and Engineering, 239, Article 012006. https://doi.org/10.1088/1757-899X/239/1/012006
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3(4), 122–128. https://doi.org/10.2514/8.155
Abubakar, M. I., & Wang, Q. (2018b). Modelling and simulation of ageing on performance of assembly workers through a learning curve. International Journal of Modeling and Optimization, 8(3), 183–187. https://doi.org/10.7763/IJMO.2018.V8.646
Abubakar, M. I., & Wang, Q. (2019, April). Integrating human factor decision components into a des model. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 562–566). IEEE. https://doi.org/10.1109/IEA.2019.8714774
Arzet, H. (2005). Grundlagen des One-piece-flow. Leitfaden zur Planung und Realisierung von mitarbeitergebundenen Produktionssystemen. Rhombos Verlag.
Baines, T., Mason, S., Siebers, P. O., & Ladbrook, J. (2004). Humans: the missing link in manufacturing simulation? Simulation Modelling Practice and Theory, 12(7–8), 515–526. https://doi.org/10.1016/S1569-190X(03)00094-7
Centobelli, P., Cerchione, R., & Murino, T. (2016). Layout and material flow optimization in digital factory. International Journal of Simulation Modelling, 15(2), 223–235. https://doi.org/10.2507/IJSIMM15(2)3.327
Dar-El, E. (2000). Human learning. Springer.
De Greiff, M. (2001). Die Prognose von Lernkurven in der manuellen Montage unter besonderer Berücksichtigung der Lernkurven von Grundbewegungen. VDI Verlag.
De Jong, J. R. (1957). The effects of increasing skill on cycle time and its consequences for time standards. Ergonomics, 1(1), 51–60. https://doi.org/10.1080/00140135708964571
Dode, P., Greig, M., Zolfaghari, S., & Neumann, W. P. (2016). Integrating human factors into discrete event simulation: A proactive approach to simultaneously design for system performance and employees’ well being. International Journal of Production Research, 54(10), 3105–3117. https://doi.org/10.1080/00207543.2016.1166287
Duisberg, M., Kranz, M., Khabbazan, M., & Mütze-Niewöhner, S. (2021). Set of flexible models to support simulation-based assembly planning in SMEs. In 2021 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 63–67). IEEE.
Duisberg, M., Kranz, M., Steireif, N., Koebele, E., Nitsch, V., & Mütze-Niewöhner, S. (2022). Integration of human learning and experience in an assembly simulation model. In 36th Annual European Simulation and Modelling Conference 2022. https://doi.org/10.1109/IEEM50564.2021.9672901
Eversheim, W., Witte, K. W., & Peffekoven, K. H. (1981). Montage richtig planen Methoden und Hilfsmittel zur rationellen Gestaltung der Montage in Unternehmen mit Einzel-und Serienfertigung. Verein Deutscher Ingenieure.
Fletcher, S. R., Johnson, T., Adlon, T., Larreina, J., Casla, P., Parigot, L., Alfaro, P. J. & del Mar Otero, M. (2020). Adaptive automation assembly: Identifying system requirements for technical efficiency and worker satisfaction. Computers & Industrial Engineering, 139, Article 105772. https://doi.org/10.1016/j.cie.2019.03.036
Frey, S., Diawati, L., & Cakravastia, A. (2011). Learning curves in automobile assembly line. In International Conference on Industrial Engineering Theory, Applications and Practice, 16.
Greasley, A., & Owen, C. (2018). Modelling people’s behaviour using discrete-event simulation: A review. International Journal of Operations & Production Management, 38(5), 1228–1244. https://doi.org/10.1108/IJOPM-10-2016-0604
Halim, N. N. A., Shariff, S. S. R., & Zahari, S. M. (2020). Modelling an automobile assembly layout plant using probabilistic functions and discrete event simulation. International Conference on Industrial Engineering and Operations Management, (5), 2726–2737.
Hopko, S., Wang, J., & Mehta, R. (2022). Human factors considerations and metrics in shared space human-robot collaboration: A systematic review. Frontiers in Robotics and AI, 9. https://doi.org/10.3389/frobt.2022.799522
Jeske, T. (2013). Entwicklung einer Methode zur Prognose der Anlernzeit sensumotorischer Tätigkeiten [Doctoral dissertation]. Industrial Engineering and Ergonomics. Lehrstuhl und Institut für Arbeitswissenschaft, RWTH-Aachen University.
Kampker, A., Burggräf, P., Wesch-Potente, C., Petersohn, G., & Krunke, M. (2013). Life cycle oriented evaluation of flexibility in investment decisions for automated assembly systems. CIRP Journal of Manufacturing Science and Technology, 6(4), 274–280. https://doi.org/10.1016/j.cirpj.2013.07.004
Kranz, M., Duisberg, M., Harlacher, M., & Mütze-Niewöhner, S. (2021). Simulation in der Praxis: Hindernisse überwinden. Zeitschrift für wirtschaftlichen Fabrikbetrieb, 116(3), 111–114. https://doi.org/10.1515/zwf-2021-0021
Kuhlenbäumer, F. (2020). Altersdifferenzierte Analyse und Modellierung von Prozessparametern bei der Anlernung von Montageaufgaben [Doctoroal dissertation]. Industrial Engineering and Ergonomics 34. Lehrstuhl und Institut für Arbeitswissenschaft, RWTH-Aachen.
Li, Y., He, N., & Thang, D. Z. (2019). Simulation based layout design and optimization for assembly line system. In Proceedings of the World Congress on Engineering 2019.
Liebau, H. (2002). Die Lernkurvenmethode. Ergonomia, Stuttgart.
Nembhard, D. A. (2014). Cross training efficiency and flexibility with process change. International Journal of Operations & Production Management, 34(11), 1417–1439. https://doi.org/10.1108/IJOPM-06-2012-0197
Neumann, W. P., & Medbo, P. (2016). Simulating operator learning during production ramp-up in parallel vs. serial flow production. International Journal of Production Research, 55(3), 845–857. https://doi.org/10.1080/00207543.2016.1217362
Ranasinghe, T., Senanayake, C. D., & Perera, K. (2018). Effects of non-homogeneous learning on the performance of serial production systems – A simulation study. In MERCon 2018: 4th International Multidisciplinary Engineering Research Conference. Civil Engineering Complex, University of Moratuwa, Sri Lanka. IEEE. https://doi.org/10.1109/MERCon.2018.8421995
Ullrich, G. (1995). Wirtschaftliches Anlernen in der Serienmontage: ein Beitrag zur Lernkurventheorie [Doctoral dissertation]. Berichte aus der Fertigungstechnik. Gerhard-Mercator-Universität Duisburg.
Wang, Q., Sowden, M., & Mileham, A. R. (2013). Modeling Human Performance within an automotive engine assembly line. The International Journal of Advanced Manufacturing Technology, (1), 141–148. https://doi.org/10.1007/s00170-012-4714-y
Wang, Q., & Abubakar, M. I. (2017). Human factors and their effects on human-centred assembly systems – A literature review-based study. IOP Conference Series: Materials Science and Engineering, 239, Article 012006. https://doi.org/10.1088/1757-899X/239/1/012006
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3(4), 122–128. https://doi.org/10.2514/8.155