摘要
结合我国制造业实际生产状况,针对柔性作业车间跨单元调度问题,提出一种基于K-means聚类的超启发式算法。应用K-means聚类算法将相近属性的实体划入相应“工件簇”决策块中,采用蚁群算法为每个决策块选择启发式规则;对每个决策块内的实体运用相应的启发式规则产生调度解。仿真结果表明:该算法以决策块的形式适度增大了计算粒度,有效降低了算法时间复杂度,以聚类的方式将具有相近属性的被加工实体进行聚集,有利于为不同属性的实体选择合适的规则。该算法提高了计算效率,具有较好的优化性能,是解决柔性跨单元调度的一种有效算法。
According to the actual production situation of China's manufacturing industry,a hyper-heuristic algorithm based on K-means clustering is proposed for inter-cell scheduling problem of flexible job-shop.K-means clustering is applied to group entities with similar attributes into the corresponding work cluster decision blocks,and the ant colony algorithm is used to select heuristic rules for each decision block.The optimal scheduling solutions are generated by using corresponding heuristic rules for scheduling of entities in each decision block.Computational results show that,the computational granularity is properly increased by the form of decision blocks,and the computational efficiency of the optimal algorithm is improved.The clustering algorithm could group the processed entities with similar attributes and the suitable rules for entities with different attributes are easy to be chosen.The proposed approach not only improves computational efficiency but also exhibits good optimization performance,and provides a scientific optimization solution for inter-cell scheduling problems.
作者
赵彦霖
田云娜
Zhao Yanlin;Tian Yunna(School of Mathematics and Computer Science,Yan'an University,Yan'an 716000,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第4期941-956,共16页
Journal of System Simulation
基金
国家自然科学基金(61763046,62041212)。
关键词
跨单元调度
超启发式算法
决策块
聚类
蚁群算法
inter-cell scheduling
hyper-heuristic algorithm
decision block
clustering
ant colony optimization
