【摘要】 文章主要研究多目标的柔性车间调度问题。在实际生产过程中，调度结果受完工时间、机器负荷、成本控制和资源消耗等多方面因素影响，因此提出了一种基于多目标优化的改进遗传算法，针对最小化最大完成时间、最小化机器负荷和最小化资源消耗3个目标函数进行优化，结合改进的Pareto多目标优化方法，以及最短加工时间变异和邻域变异方法，提高了算法的寻优能力。最后通过实验验证了算法适用于求解多目标的柔性车间调度问题。更多还原

【Abstract】 This paper focuses on the multi-objective flexible job-shop scheduling problem.Because in the actual production process,the scheduling results from completion time,the machine load,cost control,resource consumption and other factors influence,so this paper puts forward a kind of improved genetic algorithm based on multi-objective optimization,to minimize the maximum completion time,minimize the machine load and minimize resource consumption three objective function optimization,combined with the improved Pareto multi-objective optimization method,the shortest processing time variation method and the neighborhood variation method,the optimization ability of the algorithm is improved.Finally,the experimental results show that the proposed algorithm is suitable for solving multiobjective flexible job-shop scheduling problem.更多还原