Assembly lines are characterised by high rates of turnover and absenteeism. Any case of absenteeism or turnover requires assigning a replacement worker who is often inexperienced. Learning process is crucial for increasing productivity in such replacement cases, but learning is dependent on the variety of product models produced on that line. The complexity effect of the tasks at the assembly station, owing to a multi-model pattern, can result in a forgetting curve. The current research investigates the absenteeism in various batch sizes of multi-model and mixed-model assembly lines, introducing an innovative adaptation of the learning and forgetting functions. Secondly, it analyses the assembly system performance through a simulation study, focusing on the models' commonality and models sequences in the case of new substitute workers. A case study and a simulation analysis are reported to validate the research.
|Number of pages||25|
|Journal||International Journal of Industrial and Systems Engineering|
|State||Published - 2022|
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