As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In order to balance production and green sustainable development, this paper puts forward man–machine dual resource constraint mechanism. In addition, a multi-objective flexible job shop scheduling problem model constrained by job transportation time and learning effect is constructed, and the goal is to minimize processing time energy consumption and noise. Subsequently, a hybrid discrete multi-objective imperial competition algorithm (HDMICA) is developed to solve the model. The global search mechanism based on the HDMICA improves two aspects: a new initialization method to improve the quality of the initial population, and the empire selection method based on Pareto non-dominated solution to balance the empire forces. Then, the improved simulated annealing algorithm is embedded in imperial competition algorithm (ICA), which overcomes the premature convergence problem of ICA. Therefore, four neighborhood structures are designed to help the algorithm jump out of the local optimal solution. Finally, an example is used to verify the feasibility of the proposed algorithm. By comparing with the original ICA and other four algorithms, the effectiveness of the proposed algorithm in the quality of the first frontier solution is verified.

作为高能耗，高污染的制造业之一，砂型铸造在绿色制造中面临着重大挑战。为了平衡生产与绿色可持续发展，本文提出了人机双重资源约束机制。此外，构建了一个受作业运输时间和学习效果约束的多目标柔性作业车间调度问题模型，其目的是最大程度地减少加工时间的能耗和噪声。随后，开发了一种混合离散多目标帝国竞争算法（HDMICA）来求解该模型。基于HDMICA的全局搜索机制从两个方面进行了改进：一种用于提高初始填充质量的新初始化方法，以及基于帕累托非支配解的帝国选择方法来平衡帝国力量。然后，将改进的模拟退火算法嵌入帝国竞争算法（ICA）中，克服了ICA的过早收敛问题。因此，设计了四个邻域结构来帮助算法跳出局部最优解。最后，通过一个例子验证了所提算法的可行性。通过与原始ICA和其他四种算法进行比较，验证了该算法在第一个前沿解决方案质量上的有效性。设计了四个邻域结构来帮助算法跳出局部最优解。最后，通过一个例子验证了所提算法的可行性。通过与原始ICA和其他四种算法进行比较，验证了该算法在第一个前沿解决方案质量上的有效性。设计了四个邻域结构来帮助算法跳出局部最优解。最后，通过一个例子验证了所提算法的可行性。通过与原始ICA和其他四种算法进行比较，验证了该算法在第一个前沿解决方案质量上的有效性。