Two-sided robotic assembly lines are employed to assemble large-sized high-volume products, where robots are allocated to the workstations to perform the tasks and human workers are replaced for achieving lower cost and greater flexibility in production. In the two-sided robotic assembly lines, setup times are unavoidable and it has been ignored in most of the reported works. There has been limited attention on this till date. This paper focusses on the robotic two-sided assembly line with consideration of sequence-dependent setup times and robot setup times. A new mixed integer linear programming model is developed with the objective of optimizing the cycle time. Due to the NP-hard nature of the considered problem, this paper proposes a set of metaheuristics to solve this considered problem, where two main scenarios with low and high setup time’s variability are considered. Computational results verify that this new model is capable to achieve the optimal solutions for small-size instances whereas the simple adoption of the published mathematical model might produce wrong solutions for the considered problem. A comprehensive study with 13 algorithms demonstrates that the two variants of artificial bee colony algorithm and migrating bird optimization algorithm are capable to achieve the optimality for small-size instances and to obtain promising results for large-size instances.

双面机器人装配线用于组装大尺寸、大批量的产品，将机器人分配到工作站执行任务，并取代人工，以实现更低的成本和更高的生产灵活性。在双面机器人装配线中，设置时间是不可避免的，并且在大多数报道的工作中都被忽略了。迄今为止，对此的关注有限。本文重点关注机器人两侧装配线，并考虑了顺序相关的设置时间和机器人设置时间。开发了一种新的混合整数线性规划模型，其目标是优化循环时间。由于所考虑问题的 NP 困难性质，本文提出了一组元启发法来解决所考虑的问题，其中考虑了具有低和高设置时间可变性的两种主要场景。计算结果验证了这个新模型能够为小规模实例实现最佳解决方案，而简单采用已发布的数学模型可能会为所考虑的问题产生错误的解决方案。对13种算法的综合研究表明，人工蜂群算法和候鸟优化算法的两种变体能够在小尺寸实例上实现最优性，并在大尺寸实例上获得有希望的结果。