A genetic algorithm for robotic assembly line balancing
A genetic algorithm for robotic assembly line balancing
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DOI：
10.1016/j.ejor.2004.07.030
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发表时间：
2006-02
期刊：
影响因子：
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通讯作者：
G. Levitin;J. Rubinovitz;B. Shnits
中科院分区：
文献类型：
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作者：
G. Levitin;J. Rubinovitz;B. Shnits
通过使用机器人可以实现装配线的灵活性和自动化。机器人装配线平衡（RALB）问题是针对机器人装配线定义的，其中不同的机器人可能被分配到装配任务，并且由于其能力和专业性，每个机器人需要不同的装配时间来执行给定的任务。 RALB问题的解决方案包括尝试将机器人优化分配到线路站点以及在不同站点之间平衡分配工作。它的目的是最大限度地提高生产线的生产率。遗传算法（GA）用于找到该问题的解决方案。引入了两种不同的程序，通过将具有不同能力的机器人分配给工作站来使 GA 适应 RALB 问题：递归分配程序和连续分配程序。遗传算法的结果通过局部优化（爬山）工件交换程序得到改善。对一组随机生成的问题进行的测试表明，连续分配过程通常可以实现更好的解决方案质量（通过平均周期时间来衡量）。进行进一步的测试以确定 GA 程序的最佳参数组合。将 GA 算法结果与针对 RALB 问题的截断分支定界算法进行比较，表明 GA 始终给出更好的结果。
Flexibility and automation in assembly lines can be achieved by the use of robots. The robotic assembly line balancing (RALB) problem is defined for robotic assembly line, where different robots may be assigned to the assembly tasks, and each robot needs different assembly times to perform a given task, because of its capabilities and specialization. The solution to the RALB problem includes an attempt for optimal assignment of robots to line stations and a balanced distribution of work between different stations. It aims at maximizing the production rate of the line. A genetic algorithm (GA) is used to find a solution to this problem. Two different procedures for adapting the GA to the RALB problem, by assigning robots with different capabilities to workstations are introduced: a recursive assignment procedure and a consecutive assignment procedure. The results of the GA are improved by a local optimization (hill climbing) work-piece exchange procedure. Tests conducted on a set of randomly generated problems, show that the Consecutive Assignment procedure achieves, in general, better solution quality (measured by average cycle time). Further tests are conducted to determine the best combination of parameters for the GA procedure. Comparison of the GA algorithm results with a truncated Branch and Bound algorithm for the RALB problem, demonstrates that the GA gives consistently better results.