【摘要】 文章以BY公司为背景，研究液压元件装配流水车间调度问题(hydraulic components assembly flowshop scheduling problem, HCAFSP)。通过分析两阶段装配流水车间调度(the two-stage assembly flowshop scheduling problem, TSAFSP)、液压元件生产工艺与车间设施，提出一种新的机器配置和在制品库存计算方法。考虑到该问题的NP难性与BY公司数字化车间升级带来的产能提升，设计一种遗传算法孤岛模型(genetic algorithm island model, IsLandGA)与粒子群优化(particle swarm optimization, PSO)的混合算法(IsLandGA-PSO)。该算法采用圆锥拓扑，圆锥底为IsLandGA,提供全局快速搜索能力；顶点为每个子群的最优个体组成的PSO,提供精准局部搜索能力；利用优势互补和迁移优秀个体完成协同进化。通过多组实例仿真与算法对比，表明该算法求解较大规模HCAFSP问题的有效性和先进性。更多还原

【Abstract】 This paper takes BY Company as the background to study the hydraulic components assembly flowshop scheduling problem(HCAFSP). By analyzing the two-stage assembly flowshop scheduling problem(TSAFSP), production process of hydraulic components and workshop facilities, this paper proposes a new machine configuration and work in process inventory calculation method. Considering the NP-hardness of the problem and the capacity increase brought by the upgrading of digital workshop in BY Company, a hybrid algorithm(IsLandGA-PSO) of genetic algorithm island model(IsLandGA) and particle swarm optimization(PSO) is designed. The algorithm uses a cone topology, the bottom of the cone is IsLandGA, providing global fast search capabilities; the vertex is the PSO composed of the best individuals in each subgroup, providing precise local search capabilities; complementary advantages and outstanding individual migration are used to complete co-evolution. Through multiple sets of example simulation and algorithm comparison, the effectiveness and advancement of the algorithm in solving large-scale HCAFSP problems are proved.更多还原