Abstract
Although Mixed-Model assembly lines have a number of advantages over Single-Model lines, they suffer from several drawbacks, such as increased assembly complexity and greater work flow fluctuations, mainly due to the differences between the models assembled in the line. This research has adopted a new approach to cope with the above disadvantages in which the models are clustered into several assembly lines. The research presents a Model Partitioning and Clustering Algorithm (MPCA), which determines the similarity between models and assigns them accordingly to different parallel assembly lines. Empirical analysis shows that the MPCA can find a better solution than the optimal solution for one Mixed-Model line, which assembles all the models simultaneously.
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