Abstract: As employee cost rises,collaborative robots are increasingly utilized in assembly lines to either work alone or assist employees to complete the assembly tasks.Aiming at the man-robot cooperative assembly line balancing problem,a mixed integer programming model to optimize the cycle time was established.This formulated model was capable of solving the small-size instances optimally.Meanwhile,an enhanced artificial bee colony algorithm was developed to solve the large-size instances.The algorithm utilized two-layer encoding method of task permutation vector and robot assignment vector and one effective decoding procedure to achieve a feasible solution.The superior individuals in improved employed bee phase was preserved,and the performance of the new solution was developed in improved scout bee phase,thus the exploitation capacity of this algorithm was enhanced in local search phase.To evaluate the performance of the proposed method,it was compared with original artificial bee colony algorithm,late acceptance hill-climbing algorithm,simulated annealing algorithm,genetic algorithm,discrete particle swarm optimization algorithm and migrating birds optimization algorithm.The computational test demonstrated that the proposed method outperformed the compared methods and was capable of solving the man-robot cooperative assembly line balancing problem effectively.

Key words: assembly line balancing, man-robot collaboration, artificial bee colony algorithm, intelligent optimization algorithm

摘要： 随着员工成本的增加,协作机器人逐步应用于装配线以独立完成或者协助员工完成装配操作。针对人机协作装配线平衡问题,构建了混合整数规划模型以优化生产节拍,该模型可精确求解小规模案例,同时提出改进人工蜂群算法求解大规模问题。该算法采用操作排序向量和装配模式向量双层编码方式以及有效的解码获得可行的调度方案,并在改进的观察蜂阶段保留较优的种群,在改进的侦察蜂阶段提升新解的性能,在局部搜索阶段提高算法的局部搜索能力。最后,通过与已有算法对比,表明改进人工蜂群算法具有优越性,可以高效求解人机协作装配线平衡问题。

关键词: 装配线平衡, 人机协作, 人工蜂群算法, 智能优化算法