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.

流水线平衡通常以二进制任务站分配为前提。一些作者先前已经描述了由于部分任务分配或工作共享而导致的效率提高。但是，对于这种效率提高的内部存储要求尚未进行分析描述。本文定义了分数分配流水线平衡问题，并提出了混合整数线性规划模型来弥补这一差距。研究的灵活性提供的主要机会是增加的吞吐量，这与更高的内部存储成本相关。最坏情况的分析为起步（行长）和无节奏行（缓冲区）的这些成本定义了数学表达式。进行1050实例数据集的筛选。结果表明，部分分配通常可以以相对较低的成本实现更好的资源利用：较高的空间需求成本通常是一次性投资，而较短的循环时间则从根本上实现了持续收益。最后，对提出的公式进行了修改，并应用于工业数据。此混合模型装配线案例研究表明，分数分配还可以导致需求不确定性方面更稳健的平衡。