中文摘要：

针对遗传算法（GA）求解车间作业问题JSP（job shop problems）的早熟和收敛速度慢等问题，基于算法混合的思想，将改进的瓶颈移动算法MSB（modify shifting bottleneck）所求得的调度方案加入遗传算法解空间，参与遗传算法迭代运算，形成高性能的混合遗传算法HGA（hybrid genetic algorithm）。由于MSB所获得解的质量较高，依据遗传算法的精英保留策略，可以加快算法的收敛速度。标准实例上进行的仿真实验表明，调度结果的平均质量、最好调度的获取能力和算法稳定性方面，HGA的性能明显优于GA。

英文摘要：

Prematurity and slow convergence are two problems existing in GA （genetic algorithm） for the NP-hard JSP （job shop problems）. HGA （a hybrid genetic algorithm for job shop problems） is developed for JSP with the objective of makespan minimization which is based on hybrid algorithm ideology. A solution which is formed by modified shifting bottleneck algorithm served as one initial chromosome in GA. HGA overcomes the problems of prematurity and convergence in GA. Experimental results show that HGA can efficiently solve JSP and can obtain optimums on some instances. HGA outperforms GA in performance on average.