With the continuous advancement of intelligent manufacturing and industry 4.0, production scheduling has become a significant problem that most enterprises must deal with. Thereinto, (hybrid) multi-objective flexible job-shop scheduling problem, widely existing in the real-life manufacturing systems, is one of the NP-hard problems in various scheduling problems. Consequently, in this paper, an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm has been proposed to solve the problem. First of all, to overcome the pressure scarcity of individual selection in the Pareto dominance principle, especially in the late iteration of the algorithm, a novel V-dominance principle based on the volume enclosed by the normalized objective function values has been developed to enhance the convergence speed. Then, a hybrid variable neighborhood search (HVNS) structure is designed as a local search algorithm to amend the local search ability. Thereafter, for avoiding the loss of the partial (sub-)optimal solutions in the iteration, an elite storage strategy (ESS) is constructed to store the (sub-)optimal solutions. Additionally, we modify the internal habitat suitability index (HSI), migration and mutation operators of the NSBBO algorithm to further improve its performance. To evaluate the effectiveness of the above improved operations and the robustness of parameter setting, we compare the performances of each modified operation and critical parameter combination through multiple independent running the typical scheduling instance from the literature. The statistical results exhibit that each amended operation has a significant influence on the performance of INSBBO and its key parameter configuration is robust. Meanwhile, INSBBO has a better or similar performance among other state-of-the-art intelligent algorithms by comparing three classical benchmark scheduling datasets.

随着智能制造和工业4.0的不断发展，生产调度已成为大多数企业必须处理的重要问题。其中，在现实制造系统中广泛存在的（混合）多目标柔性作业车间调度问题是各种调度问题中的NP难题。因此，本文提出了一种改进的基于生物地理学的非支配排序优化算法（INSBBO）来解决该问题。首先，为了克服帕累托优势原则中单个选择的压力稀缺性，特别是在算法的后期迭代中，基于归一化目标函数值包围的体积，开发了一种新颖的V优势原理，以增强收敛速度。然后，将混合变量邻域搜索（HVNS）结构设计为局部搜索算法，以修正局部搜索能力。此后，为了避免部分（次）最优解在迭代中丢失，构造了精英存储策略（ESS）来存储（次）最优解。此外，我们修改了NSBBO算法的内部栖息地适应性指数（HSI），迁移和变异算子，以进一步提高其性能。为了评估上述改进的操作的有效性和参数设置的鲁棒性，我们通过多次独立运行文献中的典型调度实例，比较了每个修改后的操作和关键参数组合的性能。统计结果表明，每次修改后的操作都会对INSBBO的性能产生重大影响，并且其关键参数配置非常可靠。同时，通过比较三个经典基准调度数据集，INSBBO在其他最新的智能算法中具有更好或相似的性能。