The type-II mixed-model assembly line balancing problem with uncertain task times is a critical problem. This paper addresses this issue of practical significance to production efficiency. Herein, a robust optimization model for this problem is formulated to hedge against uncertainty. Moreover, the counterpart of the robust optimization model is developed by duality. A hybrid genetic algorithm (HGA) is proposed to solve this problem. In this algorithm, a heuristic method is utilized to seed the initial population. In addition, an adaptive local search procedure and a discrete Levy flight are hybridized with the genetic algorithm (GA) to enhance the performance of the algorithm. The effectiveness of the HGA is tested on a set of benchmark instances. Furthermore, the effect of uncertainty parameters on production efficiency is also investigated.

中文翻译：

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具有间隔任务时间的II型鲁棒混合模型装配线平衡问题的混合遗传算法

具有不确定任务时间的II型混合模型装配线平衡问题是一个关键问题。本文讨论了对生产效率具有实际意义的问题。在此，针对该问题制定了鲁棒的优化模型来对冲不确定性。而且，鲁棒性优化模型的对应物是通过对偶性开发的。提出了一种混合遗传算法（HGA）来解决这个问题。在该算法中，采用启发式方法来播种初始种群。另外，将自适应局部搜索过程和离散征费飞行与遗传算法（GA）混合，以增强算法的性能。HGA的有效性在一组基准实例上进行了测试。此外，还研究了不确定性参数对生产效率的影响。