Optimal layout and work allocation in batch assembly under learning effect

This research investigates the effect of system’s configuration (layout) and work allocation on the makespan of batches of different products characterised by significant learning. Frequently, in science-based industries the layout of the batch assembly could be relatively easily setup separately for each batch, where each batch contains different type of products. The research aims at finding guidelines for configurations and work allocations that will minimise the makespan for a given number of workers. Analytical expression of the makespan is developed and analysed.