Modelling and Simulation of Ageing on Performance of Assembly Workers through a Learning - Volume 8 Number 3 (Jun. 2018) - ijmo
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General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2018 Vol.8(3): 183-187 ISSN: 2010-3697
DOI: 10.7763/IJMO.2018.V8.646

Modelling and Simulation of Ageing on Performance of Assembly Workers through a Learning

Maji I. Abubakar and Qian Wang
Abstract—In the past decade, the manufacturing environment has faced more challenges than ever since as a result of the increase of global competiveness and preferences of customer demands, which require developments of a resilient production system that is capable of providing essential flexibility and responsiveness to accommodate changes at an unpredictable circumstance. Human centred assembly systems, as an example, can offer such characteristics because of the nature of human intelligence and problem solving abilities. Nevertheless, human performance on a human centred assembly system is also largely affected by human factors during production. Ageing is one of human factors that may significantly affect human performance in completing assigned assembly tasks. When designing and analysing a human centred manufacturing system, such a human attribute is often inadequately represented in neither mathematical models nor computer-based simulation models and therefore the analysed outcomes using these approaches may not properly describe the real behaviour of the system. The result of the previous studies also indicates that human performance may start to decline from the age of 38 years old and beyond. This paper presents a study by investigating the influence of ageing on assembly worker performance using a learning curve approach. The different ageing cohorts were incorporated into a DES (discrete event simulation) model. The study concludes that worker productivity decreases by an average 1% per year as the age of workers increases from 38 to 70 years old.

Index Terms—Modelling simulation, learning curve, human factors, assembly systems.

Maji I. A. and Qian W. are with School of Engineering, University of Portsmouth, Portsmouth, PO1 3DJ UK (e-mail: maji.abubakar@myport.ac.uk, qian.wang@port.ac.uk).

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Cite: Maji I. Abubakar and Qian Wang, "Modelling and Simulation of Ageing on Performance of Assembly Workers through a Learning," International Journal of Modeling and Optimization vol. 8, no. 3, pp. 183-187, 2018.

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