在实际生产过程中，随着设备运行时间的增长，设备性能会逐渐下降，导致加工时间延长。对此，针对考虑恶化效应的可重入混合流水车间调度问题，提出一种改进的非支配排序遗传算法（improved nondominated sorting genetic algorithm Ⅱ，INSGA-Ⅱ）。首先，构建以最大完工时间和加工能耗为优化目标的数学模型；其次，结合问题的特点，在算法采用基于工件序列的编码方式，并设计一种考虑恶化效应的节能调度解码方法；再次，为了提高种群的多样性设计多种变异算子，通过算法参数的自适应调整避免算法陷入局部最优，并设计变邻域搜索策略强化了算法的局部搜索能力；最后，通过与其他算法比较，在10个不同规模的实验算例中验证了所提算法具有较高的解集质量，同时也有较好的多样性和收敛性。

In practical production processes, equipment performance gradually degrades over time, leading to extended processing durations. To address this issue, an improved nondominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was proposed for a re-entrant hybrid flow-shop scheduling problem that considers deterioration effects. Firstly, a mathematical model was formulated with the optimization objectives of minimizing makespan and reducing processing energy consumption. Secondly, a job-sequence-based encoding method was employed, and an energy-efficient scheduling decoding method that accounts for deterioration effects was designed. Additionally, to enhance population diversity, various mutation operators were introduced, and algorithm parameters were adaptively adjusted to prevent convergence to local optima. A variable neighborhood search strategy was also integrated to reinforce the local search capability of the algorithm. Finally, comparative experiments with other algorithms on ten different scale test instances demonstrated that the proposed algorithm delivers superior solution quality, along with better diversity and convergence properties.