Abstract: Collaborative robots (Cobots) have been increasingly utilized in assembly lines to assist the human workers or complete the assembly tasks independently to reduce the human worker cost.Two mixed-integer programming models were formulated to optimize the cycle time in assembly lines with different types of cobots and parallel collaboration.These two models were capable of solving the small-size instances optimally and the improved model had the smaller solving time.Meanwhile,an improved migrating birds optimization algorithm was also developed to solve large-size problems.By adopting two-layer coding mode of task assignment vector and process alternative vector,this algorithm obtained developed a feasible scheduling scheme with a mathematical programming approach in decoding.The proposed algorithm utilized improved leader improvement phase and population improvement phase to accelerate the evolution of whole swarm and avoid being trapping in local optimum,and a restart mechanism was used to enhance the algorithm's exploration ability.To evaluate the proposed method,it was compared with simulated annealing algorithm,late acceptance hill-climbing algorithm,genetic algorithm,particle swarm optimization algorithm,original artificial bee colony algorithm and original migrating bird optimization algorithm.Computational study demonstrated that the proposed algorithm outperformed the original one and achieved promising performance in comparison with other methods.

Key words: assembly line balancing, man-robot collaboration, mathematical programming, migrating birds optimization 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。针对多种机器人和并行协作的人机协作装配线平衡问题,构建了两个混合整数规划模型以优化生产节拍。模型均可实现小规模案例的精确求解,并且改进后的模型在求解时间上明显优于改进前的模型。同时,提出了改进的候鸟迁徙算法以实现大规模问题的求解。该算法采取任务分配向量和装配模式向量双层编码方式,并在解码中采取数学规划方法获得可行的调度方案。改进的候鸟迁徙算法采取改进的领鸟进化阶段和改进的个体进化阶段以加速种群的进化并防止陷入局部最优,采取重启机制增强算法的探索能力。为了测试改进算法的性能,该算法与延迟接受爬山算法、模拟退火算法、遗传算法、离散粒子群算法、原始人工蜂群算法和原始候鸟迁徙算法进行对比,测试结果表明改进算法优于对比算法,可实现人机协作装配线平衡问题的高效求解。

关键词: 装配线平衡, 人机协作, 数学规划, 候鸟迁徙算法, 智能优化算法