Modeling human performance using learning curves
Wang, Q., Sowden, M., Mileham, A. and Owen, G., 2010. Modeling human performance using learning curves. In: Jiang, Y. and Bao, H., eds. ICMS2010: Proceedings of the Third International Conference on Modelling and Simulation, Vol 4 - Modelling and Simulation in Biology, Ecology & Environment. Vol. 4. Liverpool: World Academic Union, pp. 1-7.
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In this paper, we investigated a semi-automated automotive engine assembly line in which the traditional strategy of using fixed workers in each manual assembly section is replaced by a new strategy of using walking workers. With this approach, both worker and engine travel simultaneously down the line; each worker is previously trained to accomplish a series of assembly tasks independently from start to finish in each manual assembly section. The main problem of this design is that each worker needs to be cross-trained to acquire a satisfactory level of skills associated with the assignment of assembly tasks in order to retain a relatively even working speed between two adjacent workstations. In theory, the familiar degree of completing the assigned tasks by each worker through training can be measured and expressed as a learning curve. In this case study, the learning curve has been used to determine a trade-off decision between the complexity of assigned tasks and the duration of completing these tasks by a walking worker at a stabilized level. The paper describes a framework of assessing the human performance by modeling a learning curve for each walking worker based on an integrated model. Thus, a possible and realistic assignment of assembly tasks for a walking worker can be quantified.
|Item Type||Book Sections|
|Creators||Wang, Q., Sowden, M., Mileham, A. and Owen, G.|
|Editors||Jiang, Y.and Bao, H.|
|Departments||Faculty of Engineering & Design > Mechanical Engineering|
|Additional Information||3rd Joint International Conference on Modelling and Simulation/3rd International Conference on Information and Computing Science. 4-6 June 2010. Wuxi, China.|
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