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中文摘要：
针对有工件组调整时间的流水车间调度问题，提出了无成组技术假设条件下的多目标优化模型，并设计了一种进化计算与局部搜索结合的混合遗传算法。模型的目标函数是最小化最大完工时间和最大拖期。在局部搜索过程中，根据问题的特征定义了两种邻域结构，采取两阶段搜索策略，以提高算法的优化搜索效率。进化过程中，采用基于个体的累计排序数和密度值的适应度分配方法，以保持群体多样性，并采取精英保留策略，以保证解的收敛性。通过测试问题和实际问题的实验以及与其他算法的比较，验证了所提模型和算法的有效性。
英文摘要：
In order to deal with multi-objective flow shop scheduling problems with family setup times, an optimization model was established under the group technology assumption removed. Then, a hybrid genetic algorithm combining evolutionary computation with local search was designed to solve this model. The objective function of the model was to minimize the makespan and the maximum tardiness. Two new neighborhood structures based on the problem characteristics were defined, and a two-stage search strategy was used in the local search procedure to improve efficiency of optimization. The fitness assignment based on the double rank of the individual and its density value was employed to preserve diversity of the solutions, and the elitist strategy was adopted to ensure the conver-gence of the solutions. The performance of this algorithm was compared to three multi-objective algorithms proposed in the literature on several test problems and a practical problem, the experimental results demonstrated the effectiveness of the scheduling model and the proposed algorithm.
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