As a worker-centred assembly mode developed in the electronics industry in Japan, seru is receiving growing attention due to improved flexibility and responsiveness. In seru implementation, cross-trained worker assignment is a vital problem. Most previous studies focused on assigning cross-trained workers into pure divisional or rotating seru separately, but overlooked the problem for a hybrid seru production system that includes both seru types. This research fills this gap by minimizing the makespan and balancing the workers’ workload of each seru in a bi-objective mathematical model. For medium-scale instances, the exact solutions are obtained. For large-scale instances, we propose an NSGA-II-based memetic algorithm that uses two-level encoding and incorporates the bat algorithm as a local search and two K-means-based NSGA-II algorithms. The experimental results illustrate that the K-means-based NSGA-II not only outperforms other algorithms with respect to common proximity and diversity metrics but also runs an order of magnitude faster (in seconds versus minutes required by others on the same computer). Some management insights are obtained based on many numerical experiments.

作为在日本电子行业发展起来的以工人为中心的装配模式，seru由于提高了灵活性和响应能力而受到越来越多的关注。在seru实施中，交叉训练的工人分配是一个至关重要的问题。以前的大多数研究都侧重于将交叉培训的工人分别分配到纯部门或轮换血清中，但忽略了包括两种血清类型的混合血清生产系统的问题。这项研究通过最小化制造时间和平衡每个血清的工人工作量来填补这一空白在双目标数学模型中。对于中等规模的实例，可以获得精确解。对于大规模实例，我们提出了一种基于 NSGA-II 的模因算法，该算法使用两级编码并结合了 bat 算法作为局部搜索和两种基于 K-means 的 NSGA-II 算法。实验结果表明，基于 K-means 的 NSGA-II 不仅在常见的邻近度和多样性指标方面优于其他算法，而且运行速度也快了一个数量级（以秒为单位，而在同一台计算机上的其他人则需要以分钟为单位）。一些管理见解是基于许多数值实验获得的。