The manufacturing industry has evolved in the past few years due to the competitive global economy where the performance of its assembly line operations is primarily dependent upon optimum resource utilization. The assembly line operations are balanced among the available resources to obtain an equal amount of workload to achieve optimum resource utilization, called the assembly line balancing (ALB) problem. Various approaches have been proposed to solve the ALB problem, which is broadly categorized as exact, heuristic, and meta-heuristic approaches. Although solving the ALB problem is crucial, a bottleneck may still occur over the next operation stages. By using problem-specific information (bottleneck identification), it is expected to improve the solution quality of the ALB problem. As such, the contribution of this study is the computational method, namely as the swarm of immune cells with bottleneck identification (SIC+) approach, where both the ALB and bottleneck identification problems are addressed. In addition to the flexible problem representation, the SIC+ approach is equipped with a discrete bottleneck simulator to simulate the bottleneck scenario and bottleneck-specific operators to redistribute the machine workload of the identified bottleneck machine. The approach was tested on 24 benchmark data sets of the ALB problem, and the impact of incorporating bottleneck identification was illustrated. The experimental results show that the proposed SIC+ approach has achieved a total of 66.12% optimal solution over all instances of the benchmark data sets and has been compared with approaches from the literature where high-quality solutions were statistically justified.

由于竞争激烈的全球经济，制造业在过去几年中得到了发展，其装配线运营的绩效主要取决于最佳的资源利用。流水线操作在可用资源之间进行平衡，以获得相等数量的工作负载以实现最佳资源利用率，这称为流水线平衡（ALB）问题。已经提出了各种方法来解决ALB问题，其被广泛地分类为精确，启发式和元启发式方法。尽管解决ALB问题至关重要，但在接下来的操作阶段仍可能出现瓶颈。通过使用特定于问题的信息（瓶颈识别），有望提高ALB问题的解决质量。因此，这项研究的贡献在于计算方法，即作为具有瓶颈识别（SIC +）方法的免疫细胞群，在其中解决了ALB和瓶颈识别问题。除了灵活的问题表示方式之外，SIC +方法还配备了离散的瓶颈模拟器来模拟瓶颈情况，并配备瓶颈专用的操作员来重新分配已识别瓶颈机器的机器工作负荷。该方法在ALB问题的24个基准数据集上进行了测试，并说明了合并瓶颈识别的影响。实验结果表明，提出的SIC +方法已达到66个。