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作 者:张富强[1,2] 吴磊[1,2] 惠记庄 邵树军[3] 杜超[3] ZHANG Fuqiang;WU Lei;HUI Jizhuang;SHAO Shujun;DU Chao(Key Laboratory of Road Construction Technology and Equipment of MOE,Chang'an University,Xi'an 710064,China;Institute of Smart Manufacturing Systems,Chang'an University,Xi'an 710064,China;Shaanxi Fast Gear Co.,Ltd.,Xi'an 710119,China)
机构地区:[1]长安大学道路施工技术与装备教育部重点实验室,西安710064 [2]长安大学智能制造系统研究所,西安710064 [3]陕西法士特齿轮有限责任公司,西安710119
出 处:《北京工业大学学报》2022年第11期1132-1140,共9页Journal of Beijing University of Technology
基 金:陕西省科技重大专项资助项目(2018zdzx01-01-01);陕西省自然科学基金资助项目(2021JM-173);中央高校基金资助项目(300102250201)。
摘 要:针对柔性车间生产排程问题,以最小化完工时间、最小化机器总负载以及加工总成本最低为目标函数,设计了一种多目标麻雀搜索算法(multi-objective sparrow search algorithm,MOSSA)进行求解.首先,将父子代融合后的种群进行非支配排序,选取最优位置个体和最差位置个体;其次,采用两段式规则对机器选择和工序排序进行编码;最后,利用麻雀搜索算法完成种群的更新和寻优.通过算例进行实验仿真,研究参数对MOSSA的影响,并将MOSSA与其他算法进行比较.结果表明:该模型下,参数影响较小,MOSSA具有高性能全局搜索能力和较好的收敛性,对于解决多目标生产排程问题具有指导作用.Aiming at the issue of flexible workshop production scheduling,a multi-objective sparrow search algorithm(MOSSA)was designed to solve the problem with the objective function of minimizing the completion time,the sum of machine load and the whole machining cost.First,the population after the fusion of father and son was sorted by non dominated order,and the best position individual and the worst position individual were selected,respectively.Second,the two-stage rule was used to code the machine selection and process sequencing.Finally,the sparrow search algorithm was used to update and optimize the population.Experimental simulations were carried out through calculation examples to study the influence of parameters on MOSSA,and compared MOSSA with other algorithms.Results show that under this model,the influence of parameters is little,and MOSSA has high-performance global search capabilities and good convergence.It has a guiding role to solve the problem of multi-objective production scheduling.
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