考虑能效的多机器人协同装配线平衡方法
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摘要
为解决工位内多机器人的协同装配问题,以装配线的节拍、能源的总消耗以及机器人的总投入成本最小为优化目标,建立了工位内多机器人协同作业的装配线平衡问题的数学模型.在此基础上,提出了一种基于工位码、任务码、机器人码三层编码的多目标混合帝国竞争算法,该算法融合了非支配排序遗传算法的排序规则,并引入了延迟爬山算法,以提高算法的搜索性能.最后,对算法进行仿真实验,结果表明该算法是有效、可行的.
To achieve cooperative assembly for multi-robot in some station of an assembly line,taking the cycle time of an assembly line,the total energy consumption and the total cost of robots as the minimum optimizing objectives,a mathematical model with three objectives function was developed.To solve this problem,a multi-objective hybrid imperialist competitive algorithm(MOHICA)was proposed based on a three-level coding rule,i.e.the station level,the task level,and the robot level.In addition,a non-dominated sorted rule of genetic algorithm and a late-acceptance hill-climbing algorithm were combined into the algorithm to increase the exploration ability of the algorithm.Finally,simulation experiments and theory analysis were carried out to evaluate the proposed algorithm.The results indicate that the proposed algorithm is valid and feasible.
To achieve cooperative assembly for multi-robot in some station of an assembly line,taking the cycle time of an assembly line,the total energy consumption and the total cost of robots as the minimum optimizing objectives,a mathematical model with three objectives function was developed.To solve this problem,a multi-objective hybrid imperialist competitive algorithm(MOHICA)was proposed based on a three-level coding rule,i.e.the station level,the task level,and the robot level.In addition,a non-dominated sorted rule of genetic algorithm and a late-acceptance hill-climbing algorithm were combined into the algorithm to increase the exploration ability of the algorithm.Finally,simulation experiments and theory analysis were carried out to evaluate the proposed algorithm.The results indicate that the proposed algorithm is valid and feasible.
引文
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[2]Yoosefelahi A,Aminnayeri M,Mosadegh H,et al.TypeⅡrobotic assembly line balancing problem:an evolution strategies algorithm for a multi-objective model[J].Journal of Manufacturing Systems,2012,31(2):139-151.
[3]Nilakantan J M,Ponnambalam S G,Jawahar N.Design of energy efficient RAL system using evolutionary algorithms[J].Engineering Computations,2016,33(2):580-602.
[4]Li Z,Tang Q,Zhang L P.Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm[J].Journal of Cleaner Production,2016,135:508-522.
[5]Nilakantan J M,Li Z,Tang Q,et al.Multi-objective cooperative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems[J].Journal of Cleaner Production,2017,156:124-136.
[6]冯嘉珍,张建国,邱继伟.模拟动物行为的多目标可靠性优化设计博弈算法[J].北京理工大学学报(自然科学版),2018,38(5):449-453.Feng Jiazhen,Zhang Jianguo,Qiu Jiwei.Game algorithm of multi-objective reliability design optimization based on simulating animal behavior[J].Transactions of Beijing Institute of Technology,2018,38(5):449-453.(in Chinese)
[7]Enayatifar R,Yousefi M,Abdullah A H,et al.MOICA:a novel multi-objective approach based on imperialist competitive algorithm[J].Applied Mathematics and Computation,2013,219(17):8829-8841.
[8]Yuan B,Zhang C,Shao X.A late acceptance hill-climbing algorithm for balancing two-sided assembly lines with multiple constraints[J].Journal of Intelligent Manufacturing,2015,26(1):159-168.