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The effect of dynamic worker behavior on flow line performance

Author

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  • Digiesi, Salvatore
  • Kock, Ad A.A.
  • Mummolo, Giovanni
  • Rooda, Jacobus E.

Abstract

Human labor plays a central role in modern manufacturing systems. Therefore, a reliable description of both hardware and human components is required for the (re)design of such systems. In past work, hardware has received much attention, whereas the quantification of operator performance and its variability was often neglected. Mummolo et al. [2004. Learning and tiredness phenomena in manual operation performed in lean automated manufacturing systems: a reference model. In: International IMS Intelligent Manufacturing Systems Forum 2004, Cernobbio, CO, Italy, pp. 341-346] presented a model describing both fatigue and learning effects on worker behavior. In this paper, the authors show that dynamic worker behavior over time has a profound impact on the queuing behavior of flow lines. Simulation results show that these conclusion hold for both short- and long-term simulation models.

Suggested Citation

  • Digiesi, Salvatore & Kock, Ad A.A. & Mummolo, Giovanni & Rooda, Jacobus E., 2009. "The effect of dynamic worker behavior on flow line performance," International Journal of Production Economics, Elsevier, vol. 120(2), pages 368-377, August.
    • Handle: RePEc:eee:proeco:v:120:y:2009:i:2:p:368-377
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    References listed on IDEAS

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    1. Zulch, Gert & Rottinger, Sven & Vollstedt, Thorsten, 2004. "A simulation approach for planning and re-assigning of personnel in manufacturing," International Journal of Production Economics, Elsevier, vol. 90(2), pages 265-277, July.
    2. Croci, F. & Perona, M. & Pozzetti, A., 2000. "Work-force management in automated assembly systems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 243-255, March.
    3. Buzacott, John A., 2002. "The impact of worker differences on production system output," International Journal of Production Economics, Elsevier, vol. 78(1), pages 37-44, July.
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    Cited by:

    1. Mossa, G. & Boenzi, F. & Digiesi, S. & Mummolo, G. & Romano, V.A., 2016. "Productivity and ergonomic risk in human based production systems: A job-rotation scheduling model," International Journal of Production Economics, Elsevier, vol. 171(P4), pages 471-477.
    2. Patricia Heuser & Peter Letmathe & Matthias Schinner, 2022. "Workforce planning in production with flexible or budgeted employee training and volatile demand," Journal of Business Economics, Springer, vol. 92(7), pages 1093-1124, September.
    3. Ece Sancı & Meral Azizoğlu, 2017. "Rebalancing the assembly lines: exact solution approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 5991-6010, October.
    4. Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    5. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    6. Battini, Daria & Berti, Nicola & Finco, Serena & Zennaro, Ilenia & Das, Ajay, 2022. "Towards industry 5.0: A multi-objective job rotation model for an inclusive workforce," International Journal of Production Economics, Elsevier, vol. 250(C).
    7. Heuser, Patricia & Tauer, Björn, 2023. "Single-machine scheduling with product category-based learning and forgetting effects," Omega, Elsevier, vol. 115(C).

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