U-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values.

中文翻译：

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最大化生产率的U型装配线平衡问题的高效分组遗传算法

U型装配线是可以提高公司生产效率的重要工具之一。在这项研究中，为II型U线平衡问题建模使用了针对装配线平衡问题提出的两种不同的建模方法，并且已将这些模型的性能进行了比较。已经表明，使用数学公式来解决中型和大型问题实例是不切实际的，因为该问题是NP难题。因此，已经开发了一种分组遗传和模拟退火算法，并采用了粒子群优化算法来与所提出的方法进行比较。对于拟议的分组遗传算法，已经提出了一个总是获得可行后代的特殊交叉算子。此外，应用了基于特定问题知识的本地搜索过程来增加算法的强度。解决了一组著名的基准实例，以评估所提出和现有方法的有效性。结果表明，尽管数学公式只能用于解决小尺寸实例，但元启发法可以在可接受的CPU时间内为所有尺寸问题实例获得高质量的解决方案。此外，根据最佳解的数量或与下限值的偏差，已发现分组遗传算法优于其他方法。结果表明，尽管数学公式只能用于解决小尺寸实例，但元启发法可以在可接受的CPU时间内为所有尺寸问题实例获得高质量的解决方案。此外，根据最佳解的数量或与下限值的偏差，已发现分组遗传算法优于其他方法。结果表明，尽管数学公式只能用于解决小尺寸实例，但元启发法可以在可接受的CPU时间内为所有尺寸问题实例获得高质量的解决方案。此外，根据最佳解的数量或与下限值的偏差，已发现分组遗传算法优于其他方法。