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作 者:赵付青[1] 杜松霖 曹洁[1] 唐建新[1] ZHAO Fuqing;DU Songlin;CAO Jie;TANG Jianxin(College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China)
机构地区:[1]兰州理工大学计算机与通信学院,甘肃兰州730050
出 处:《华中科技大学学报(自然科学版)》2022年第5期138-142,148,共6页Journal of Huazhong University of Science and Technology(Natural Science Edition)
基 金:国家自然科学基金资助项目(62063021);甘肃省重点人才资助项目(ZZ2021G50700016);甘肃省重点研发资助项目(21YF5WA086);兰州市科技局资助项目(2018-rc-98).
摘 要:针对以装配完成时间为优化目标的分布式装配阻塞流水车间调度问题(DABFSP),提出一种协同帝王蝶优化(CMBO)算法.在算法的初始化阶段,CMBO有效利用分布式装配阻塞流水车间调度问题的特征,采用构造式的方法产生可行调度序列,并作为算法的初始解;在迭代过程中,CMBO利用两种协同的离散化算子更新种群;在局部搜索阶段,CMBO利用最优解的邻域信息进一步提升解的精度与质量.在以不同工件数、机器数、工厂数和产品数为组合的900个问题实例中,测试和比较了CMBO算法及其他先进对比算法的性能.实验结果及统计学分析表明:CMBO算法在求解分布式装配阻塞流水车间调度问题时优于其他两种对比算法.A cooperative monarch butterfly optimization(CMBO)algorithm was proposed to solve the distributed assembly blocking flow shop scheduling problem(DABFSP)with the optimization objective of minimizing the assembly completion time.In the initialization stage of the algorithm,the characteristics of DABFSP were effectively utilized by CMBO,and a constructive method was proposed to generate a feasible scheduling sequence as the initial solution of the algorithm.In the iteration process,two kinds of coordinated discretization operator were employed to update the population.In the local search stage,the neighborhood information of the optimal solution was utilized to further improve the accuracy and quality of solutions.In 900 instances with different numbers of jobs,machines,factories and products,the performance of CMBO and other advanced comparison algorithms was tested and compared.Experimental results and statistical analysis show that the CMBO algorithm is superior to the other two comparison algorithms in solving the DABFSP.
关 键 词:分布式装配阻塞流水车间调度 帝王蝶优化算法 最大装配完成时间 群智能优化算法 编码解码机制
分 类 号:TP305[自动化与计算机技术—计算机系统结构]
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