混合狼群算法在动态车间调度中的应用  

Application of Hybrid Grey Wolf Optimizer in dynamic workshop scheduling problem

作　　者：刘微[1] 兰图 孙梓华 杨硕 马腾飞 LIU Wei;LAN Tu;SUN Zi-hua;YANG Shuo;MA Teng-fei(College of Information and Technology,Jilin Normal University,Siping 136000,China)

机构地区：[1]吉林师范大学信息技术学院,吉林四平136000

出　　处：《吉林师范大学学报（自然科学版）》2021年第4期66-73,共8页Journal of Jilin Normal University:Natural Science Edition

基　　金：国家自然科学基金项目(22075101)。

摘　　要：随着全世界制造业快速发展,车间生产调度的研究和生产过程的优化得到了广泛的关注.如今应将先进的信息技术融入制造业,以期达到更高效率化的要求.灰狼算法寻优精度高、参数少、收敛速度快、鲁棒性强且具有并行性,可以提高算法的效率,但其稳定性不高、过于分散且很难找到全局最优解.而差分进化算法可以进行并行随机搜索操作,其收敛速度快、全局搜索能力强且稳定性高.两者混合可以提高全局搜索能力,大大提高算法稳定性、最优值以及收敛性能.为了验证其在车间调度问题中的性能,结合灰狼算法和差分算法的优缺点,采用混合灰狼算法对作业车间调度问题进行了改进优化处理.With the rapid development of manufacturing industry all over the world,the research of workshop production scheduling and the optimization of production process have received extensive attention.Advanced information technology should now be integrated into the manufacturing industry to achieve higher efficiency requirements.Grey Wolf Optimizer has high optimization accuracy,fewer parameters,fast convergence speed,strong robustness and parallelism,thus improving the efficiency of the algorithm.However,its stability is not high and it is too scattered,so it is difficult to find the global optimal solution.Differential Evolution Algorithm can carry out parallel random search operation.The algorithm has fast convergence speed,strong global searching ability and high stability.The combination of the two algorithms can improve the global search ability,algorithm stability,optimal value and convergence performance.In order to verify its performance in the job shop scheduling problem,the paper combined the advantages and disadvantages of the Grey Wolf Optimizer and the Differential Evolution Algorithm,and adopted the Hybrid Grey Wolf Optimizer to improve and optimize the job shop scheduling problem.

关 键 词：灰狼算法 差分算法 混合灰狼算法 动态车间调度 重调度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]