摘要
为实现工艺设计与调度的并行分布式集成,建立了工艺规程调度仿真优化的数学模型,确定了模型的决策空间、目标函数及约束条件。提出了一种协同进化免疫遗传算法,用以同时优化零件的备选工艺规程组合和调度方案,通过工艺种群及调度种群的相互促进,实现协同进化,依据抗体的亲和力及抗体浓度来保持群体的多样性,根据抗体的激励度来进行免疫选择,采用最优解保持策略,确保算法的收敛性,考虑编码特点,工艺抗体采用均匀交叉及随机扰动变异,而调度抗体采用均匀顺序交叉及倒位变异。通过对10台设备10种零件的实例仿真,验证了算法的有效性。
To realize the concurrent distributed integration of process planning ＆ scheduling, a mathematical model of simulating optimization of process ＆ scheduling was established. The decision space, objective functions, and constraints of the model were defined. A co-evolutionary immune genetic algorithm was proposed to simultaneously optimize the combination of alternative process and production scheduling. Collaborative evolution was realized through the interaction between process population and scheduling population, the affinity and concentration of antibody were used to guarantee the diversity of population, the stimulation of antibody was used to realize immune selection, and the elitism keeping strategy was used to guarantee the convergence of the algorithm. To deal with the characteristics of codes, the uniform crossover and random disturbance mutation were used for the process antibody, whereas the uniform order crossover and reverse mutation were used for the scheduling antibody. Simulation of 10 machines and 10 parts was performed to illustrate the validity of the algorithm.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2006年第11期1807-1813,共7页
Computer Integrated Manufacturing Systems
基金
国家863/CIMS主题资助项目(2003AA411110)
教育部博士点基金资助项目(20040699025)。~~
关键词
工艺规程调度优化
免疫遗传算法
抗体浓度
抗体激励度
免疫选择
optimization of process
scheduling
immune genetic algorithm
concentration of antibody
stimulation of antibody
immune selection
