中文摘要：

针对柔性作业车间调度问题中最大完工时间、机器最大负荷和总机器负荷三项性能指标，提出一种改进的自适应交叉和变异的混合遗传算法；在基本遗传算法染色体编码的基础上，设计一种基于海明距离的调度个体差异判别方法，并通过自适应交叉阈值和动态变异概率计算提高遗传算法整个种群调度个体的多样性，防止算法过早的进入早熟；在遗传算法进化期间，对每个调度个体的进化采用变邻域搜索算法，扩大调度个体的邻域搜索范围；最后，使用文献中相同的调度实例将文章的计算结果与其它文献中的测试结果进行比较，验证了所提出的算法的可行性和有效性。

英文摘要：

To deal with the flexible job-shop scheduling problem, a self-adaptive hybrid genetic algorithm is proposed by considering the performance index of maximum completion time, maximum machine load and total load. On the basis of the chromosome coding of basic genetic algorithm for the flexible job-shop scheduling problem, a new method for discriminating differences between scheduling individuals is designed based on the Hamming distance, and the population diversity is improved by the self-adaptive threshold value for the operation of crossover and the dynamic calculation of the probability of the operation of the mutation to prevent premature convergence. During the evolu- tion of the genetic algorithm, each individual executes variable neighborhood search to enhance the local search of genetic algorithm. The self - adaptive and hybrid genetic algorithm is tested on examples taken from the literature and compared with their results. The computation results show that the self-adaptive and hybrid genetic algorithm is feasible and effective.