摘要
针对轮胎硫化车间生产特点,提出一种基于粒子群算法的车间调度方案。首先采用一种局部与全局搜索相结合的粒子群算法,引入局部极值概念对算法速度公式进行修改,避免算法早熟收敛。再与遗传算法融合,通过选择、交叉、变异算子进一步优化,使结果向最优值趋近。根据硫化车间特点,采用基于任务的编码方式,使生产任务与硫化机器一一对应。通过与其它优化算法在调度实例中的比较,验证了该算法的有效性和可行性。
Based on a particle swarm optimization algorithm, a scheduling strategy was put forward in connection with the charac- teristics of vulcanization. Firstly, the new PS0 algorithm which combining the advantages of local search and global search was introduced, the concept of local extreme is induced to modify speed formula, the advanced algorithm avoids the premature conver- gence problem effectively. Then, the new PSO algorithm combin,zd with GA operators. The method makes result approach to the best value. An encoding based on curing workshop production tasks made production tasks correspond vulcanization machine. Com- pared with other algorithms used in the vulcanization dispatching, the new PSO algorithm displays better validity and feasibility.
出处
《计算机与现代化》
2016年第10期10-14,20,共6页
Computer and Modernization
基金
山东省自然科学基金资助项目(ZR2014FL019)
山东省高等学校科技计划项目(J14LN31)
青岛市科技计划基础研究项目(13-1-4-125-jch)
绿色轮胎与橡胶协同创新中心开放课题(2014GTR0020)
关键词
硫化车间调度
粒子群算法
遗传因子
算法融合
编码方式
vulcanization dispatching
particle swarm optimization
genetic operators
algorithm fusion
encoding
