基于分层蚁群遗传算法的多目标柔性作业车间调度方法

针对离散制造柔性作业车间实际工况，提出了一种基于分层蚁群遗传算法的柔性作业车间资源驱动的多目标调度方法，其基本特征是：基于连续生产中不同调度周期剩余或空闲资源等调度相关实时信息；基于完工时间和机床负荷等多目标；采用分层蚁群-遗传混合算法进行决策，通过逐步筛选，获得优化解。该方法特别适用于车间资源变化、任务执行情况变化、急件任务必须插入等情况下的动态调度。应用标准案例并设计相关组合案例进行了测试，与MOGV混合算法相比，25%的案例计算结果优于MOGV算法，最大完工时间减少5%~7%，62.5%的案例计算结果等同MOGV算法。因此，该智能调度方法不仅可以有效地取得对指定优先目标的最佳优化效果，且可自动获得多目标综合的最优解，智能调度效果显著。

Hierarchical Ant-Genetic Algorithm-based Multi-objective Intelligent Approach for Flexible Job Shop Scheduling

A hierarchical ant-genetic algorithm-based multi-objective intelligent scheduling algorithm was proposed for flexible job shop problem. Its basic features were: (1) the approach was based on the real-time resource information of different scheduling periods; (2) its targets were completion time and machine load etc.; (3) the multi-objective optimization strategy and method were used in an ant-genetic hybrid algorithm to obtain the optimal solution. This method could be used in the periodical normal scheduling, the dynamic scheduling scenario and the situation of urgent jobs inserting. Some tests were done on the standard cases and a combined case. Compared to MOGV hybrid algorithm, the proposed approach outperformed in 25% of the test cases with a 5%~7% decrease in completion time. As for rests 75% of test cases, the above two algorithms show the same results. Therefore, with the ability of optimizing results based on the priorities of objectives and the comprehensive performance of all objective automatically, the effectiveness of the method proposed in this paper was verified.