改进DE算法求解混合流水车间负荷平衡问题  

Improved DE algorithm for hybrid flow shop load balancing scheduling problem

作　　者：韩忠华[1,2] 董晓婷[1] 史海波[2] 朱一行 

机构地区：[1]沈阳建筑大学信息与控制工程学院,辽宁沈阳110168 [2]中国科学院沈阳自动化研究所,辽宁沈阳110016

出　　处：《计算机集成制造系统》2016年第2期547-557,共11页Computer Integrated Manufacturing Systems

基　　金：国家重大科技专项资助项目(2011ZX02601-005);辽宁省教育厅资助项目(L2013237)~~

摘　　要：为解决混合流水车间不相关并行机负荷平衡排产优化问题,建立了混合流水车间负荷平衡优化问题数学模型,以并行工位加工时间负荷平衡代价与总工位等待时间加权求和之值作为负荷平衡评价指标。全局优化算法采用双种群自适应差分进化算法,该算法设计了新的双种群结构和协同进化方式,并引入随停止代数自适应调整进化参数的策略,以增强跃出局部极值、保持进化活力的能力。为进一步提高算法搜索最优解效率,设计了一种基于负荷平衡选择概率的初始种群建立方法,以提高初始种群中初始解的质量、缩小有效解空间。基于汽车生产中的实例数据,将双种群自适应差分进化算法与遗传算法、差分进化算法、自适应差分进化算法进行仿真比较,结果表明,双种群自适应差分进化算法的负荷平衡评价指标有显著的降低。To solve the Hybrid Flow Shop with non-identical parallel machine Load Balancing Scheduling Problem（HFS-LBSP）,a mathematical model was formulated.The weighted summation of processing time load balancing cost and total parallel machine waiting time was taken as the load balancing comprehensive evaluation index.A Double Population Self-adaptive Differential Evolution（DPSADE）algorithm was proposed for global optimization purpose.In DPSADE algorithm,a new bi-population construction methodology and cooperative evolution mechanism was devised for the usage of maintaining the population diversity and evolution energy,and a self-adaptive parameter adjusting strategy in terms of stop iterations was introduced to enhance the capacity in escaping from the local optimal and keeping evolution alive.To improve the searching efficiency of the algorithm,a new population initialization method based on load balancing selection probability was designed at the initialization stage.Two example of production scheduling problem for car manufacturing and steel smelting processing were simulated,and the results showed that the load balance evaluation index of DPSADE algorithm had a significant reduction compared with Genetic Algorithm（GA）,Differential Evolution algorithm（DE）and Self-Adaptive Differential Evolution algorithm（SADE）.

关 键 词：混合流水车间排产问题 负荷平衡 选择概率 差分算法 双种群自适应差分进化算法 

分 类 号：TH186[机械工程—机械制造及自动化]