Assembly line balancing with fractional task allocations

Abstract

Assembly line balancing usually presupposes binary task-station assignments. Some authors have previously described efficiency increases due to fractional task allocations or work-sharing. However, the internal storage requirements for such efficiency increases have not been analytically described. This paper defines the Fractional Allocation Assembly Line Balancing Problem and presents mixed-integer linear programming models to bridge that gap. The main opportunity afforded by the studied flexibility is increased throughput, which is associated to higher internal storage costs. Worst-case analyses define mathematical expressions for these costs both for paced (line length) and unpaced lines (buffers). A screening on a 1050-instance dataset is conducted. Results suggest that fractional allocations can often allow better resource utilisation with relatively low costs: the higher space requirement costs are often one-time investments, while lower cycle time represents fundamentally continuous gains. Lastly, the proposed formulation was adapted and applied to industrial data. This mixed-model assembly line case study suggests that fractional allocations can also lead to more robust balancing regarding demand uncertainty.