Assembly line balancing with fractional task allocations
研究了允许任务分数分配的装配线平衡问题，建立了混合整数线性规划模型，分析了提高吞吐量所需的内部存储成本，并通过大规模数据集和工业案例验证了分数分配能以较低成本提升资源利用率。
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.