摘要
针对汽车模具铸造中一类带有单处理机和并行批处理机的混合加工模式,构建了以最小化最大完工时间为优化目标的带有并行批处理机的柔性作业车间调度模型。设计了一种改进遗传算法来求解该模型,在算法中提出了一种分块式集成解码规则,保证能在一次解码中得到混合活动调度方案。设计了一种聚类的选择算子,采用K-Means算法并根据适应度值将各个可行解分为多个性质相同的子类,选择留下不同子类中的染色体,且不同子类之间进行染色体交叉,拓宽种群的多样性。此外,为了改善算法的局部能力,设计了基于关键路径的局部搜索策略。仿真实验验证了所提出算法解决此类问题的可行性和有效性。
For a kind of mixed processing mode with single processor and parallel batch processor in automobile die casting,a flexible job shop scheduling model with a parallel batch machine with an optimization goal of minimizing the maximum completion time is constructed.Based on this model,an improved genetic algorithm is designed to solve the problem.A block-based integrated decoding rule is proposed to ensure that the mixed activity scheduling scheme can be obtained in one decoding.A clustering selection operator is designed.The K-Means algorithm is used to divide each feasible solution into multiple sub-categories of the same nature according to the fitness value,and chromosomes form different sub-categories make crossover to rich population diversity.In addition,in order to improve the local ability of the algorithm,a local search strategy based on the critical path is designed.The feasibility and effectively of the proposed optimization model solved by proposed algorithm is demonstrated by simulation experiments.
作者
刘蓉
周林
王朝
唐红涛
张海涛
LIU Rong;ZHOU Lin;WANG Chao;TANG Hongtao;ZHANG Haitao(School of Mechanical and Electronic EngineeringWUT, Wuhan 430070, China;不详)
出处
《武汉理工大学学报（信息与管理工程版）》
CAS
2020年第1期36-43,共8页
Journal of Wuhan University of Technology：Information & Management Engineering
基金
国家自然科学基金项目(51705384)。
关键词
柔性作业车间调度
混合活动调度方案
遗传算法
并行批处理机
聚类分类
flexible job shop scheduling
mixed activity scheduling scheme
genetic algorithm
parallel batch processing machine
clustering classification
