Nowadays, many manufacturing companies are trying to improve the performance of their processes using available innovative technologies such as collaborative robots (cobots). Cobots are robots with whom no safety distance is necessary. Through cooperation with human workers, they can help increase the production speed of existing workstations. The well-known job shop scheduling problem is, therefore, extended with the addition of a cobot to the workstation assignment. The considered objective is to maximize the normalized sum of production costs and makespan. To solve this problem, we propose a hybrid genetic algorithm with a biased random-key encoding and a variable neighborhood search. The hybrid method combines the exploration aspects of a genetic algorithm with the exploitation abilities of a variable neighborhood search. The developed algorithm is applied to real-world data and artificially generated data. To demonstrate the performance of this algorithm, a constraint programming model is implemented and the results are compared. Additionally, benchmark instances from a related problem from the cobot assignment and assembly line balancing, have been solved. The results from the real-world data show how much the objective function can be improved by the deployment of additional robots. The normalized objective function could be improved by up to 54% when using five additional cobots. As a methodological contribution, the biased random-key encoding is compared with a typical integer-based encoding. A comparison with a dataset from the literature shows that the developed algorithm can compete with state-of-the-art methods on benchmark instances.

如今，许多制造公司都在尝试使用协作机器人（cobots）等可用的创新技术来提高其流程的性能。协作机器人是不需要安全距离的机器人。通过与人工合作，他们可以帮助提高现有工作站的生产速度。因此，通过在工作站分配中添加协作机器人来扩展众所周知的作业车间调度问题。考虑的目标是最大化生产成本和制造时间的标准化总和。为了解决这个问题，我们提出了一种混合遗传算法，该算法具有有偏的随机密钥编码和可变邻域搜索。混合方法将遗传算法的探索方面与可变邻域搜索的开发能力相结合。所开发的算法适用于现实世界的数据和人工生成的数据。为了证明该算法的性能，实现了约束规划模型并比较了结果。此外，还解决了来自协作机器人分配和装配线平衡的相关问题的基准实例。真实世界数据的结果显示了通过部署额外的机器人可以改善多少目标函数。当使用五个额外的协作机器人时，标准化的目标函数可以提高多达 54%。作为一种方法论贡献，将有偏随机密钥编码与典型的基于整数的编码进行了比较。与文献中的数据集进行比较表明，所开发的算法可以在基准实例上与最先进的方法竞争。