Flow Shop Scheduling (FSS) has been widely researched due to its application in many types of fields, while the human participant brings great challenges to this problem. Manpower scheduling captures attention for assigning workers with diverse proficiency to the appropriate stages, which is of great significance to production efficiency. In this paper, we present a novel algorithm called Self-encoding Barnacle Mating Optimizer (SBMO), which solves the FSS problem considering worker proficiency, defined as a new problem, Flow Shop Manpower Scheduling Problem (FSMSP). The highlight of the SBMO algorithm is the combination with the encoding method, crossover and mutation operators. Moreover, in order to solve the local optimum problem, we design a neighborhood search scheme. Finally, the extensive comparison simulations are conducted to demonstrate the superiority of the proposed SBMO. The results indicate the effectiveness of SBMO in approximate ratio, powerful stability, and execution time, compared with the classic and popular counterparts.

翻译：由于在很多领域应用了流动商店时间安排(FSS),因此已经进行了广泛的研究,同时,人类参与者也给这一问题带来了巨大的挑战。人力时间安排吸引了人们的注意力,将不同熟练程度的工人分配到适当的阶段,这对生产效率非常重要。在本文中,我们介绍了一种叫作自我编码的自译自算的“谷仓压优化剂(SBMO)”的新算法,它解决了考虑到工人熟练程度的FSS系统问题,它被界定为一个新的问题,即流动商店人力调度问题。SBMO算法的突出之处在于与编码方法、交叉和突变操作者的结合。此外,为了解决当地的最佳问题,我们设计了一个社区搜索计划。最后,进行了广泛的比较模拟,以证明拟议的SBMO的优越性。结果表明SBMO与经典和流行的对口单位相比,其大致比率、强大的稳定性和执行时间是有效的。