摘要
针对半导体制造车间产品重入机台时存在机台状态不一致,使得传统可重入调度方法难以适用的问题,根据半导体车间生产特性,提出了半导体车间多目标可重入混合流水车间调度问题,以最小化最大完工时间为基础,考虑以降低产品不合格率、减少机台工序切换次数为目标,建立此问题的多目标数学模型.提出基于实质不确定因子的最优觅食算法,采用灰色关联分析与MYCIN不确定因子的勾股模糊集的多目标处理策略,将帕累托(Pareto)解的实质不确定因子作为最优觅食算法的适应度值.编码采用基于工件号编码方案,解码通过三段式方法生成可行的调度解.通过仿真实验和半导体车间案例与其他三种算法对比,验证了所提出的模型,算法性能分析结果表明所提出的模型合理,算法具有明显优势.
In semiconductor manufacturing workshop,the machine states are often inconsistent when the products re-enter the machine. The traditional reentrant scheduling methods are unsuitable for semiconductor workshop scheduling because of the characteristic of inconsistent.According to the characteristics of semiconductor workshop,the multi-objective reentrant hybrid flow shop scheduling problem for the semiconductor manufacturing workshop was proposed.A mathematical model of this problem was built based on minimizing the maximum completion time,reducing product failure rate and decreasing machine process switching times.An optimal foraging algorithm based on substantial uncertainty factor(SUF_OFA) was proposed.In proposed algorithm,the grey correlation analyzing and the Pythagorean fuzzy set of MYCIN uncertainty factor were used to build the multi-objective processing strategy.The substantial uncertainty factors of Pareto solutions were adopted as the fitness value of the optimal foraging algorithm. The workpiece number was used as the coding scheme,and the feasible scheduling solution was decoded by the threestage decoding method.Through different experiments and a semiconductor workshop case,the proposed algorithm was compared with four other algorithms. The proposed model was verified and the performance of the proposed algorithm was analyzed. The results show that SUF_OFA has significant advantages in solving the multi-objective reentrant hybrid flow shop scheduling problem.
作者
朱光宇
贾海斌
ZHU Guangyu;JIA Haibin(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第2期122-130,共9页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
工业和信息化部2016年智能制造综合标准化与新模式应用资助项目(工信部联装[2016]213号).
关键词
可重入混合流水车间
多目标
半导体
实质不确定因子
最优觅食算法
reentrant hybrid flow shop
multi-objective
semiconductor
substantial uncertainty factor
optimal foraging algorithm
