Modelling and simulation of ageing on performance of assembly workers through a learning curve

Maji Ibrahim Abubakar, Qian Wang

Research output: Contribution to journalArticlepeer-review

97 Downloads (Pure)

Abstract

In the past decade, the manufacturing environment has faced more challenges than ever since as a result of the increase of global competitiveness 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 centered 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 centered 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 analyzing a human centered 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 behavior 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.
Original languageEnglish
Pages (from-to)183-187
Number of pages5
JournalInternational Journal of Modeling and Optimization
Volume8
Issue number3
DOIs
Publication statusPublished - 1 Jun 2018

Fingerprint

Dive into the research topics of 'Modelling and simulation of ageing on performance of assembly workers through a learning curve'. Together they form a unique fingerprint.

Cite this