摘要
以企业的实际需求为依据,建立了柔性作业车间调度问题的数学模型;针对其特点,提出一种混合元胞粒子群优化算法,通过双层编码,将工件的加工顺序与加工机器位置信息数值化表示;引入遗传算法中的交叉、变异操作,改进了粒子位置更新方法;融入变邻域算法,改善算法局部搜索能力.通过仿真实验,结果表明:算法在求解能力方面有所提升,能够有效地求解柔性作业车间调度问题.
A mathematical model of flexible job-shop scheduling problem has developed based on the actual demand of the manufacturing enterprises. Aiming at its characteristics, a hybrid cellular particle swarm optimi- zation （HCPSO） algorithm is proposed. A double-layer coding strategy is adopted to express the position in- formation numerically for the order of the workpiece and machine. The crossover and mutation operation of genetic algorithm are introduced to improve the method of update with the particle position in the base of cel- lular particle swarm optimization. At last, the variable neighborhood algorithm is infused to enhance the abili- ty of local exploration, The experiment results indicate that the solving ability of HCPSO has improved, so as to solve the flexible job shop scheduling problem effectively.
作者
吴正佳
付先旺
望芸
刘秀凤
Wu Zhengjia Fu Xianwang Wang Yun Liu Xiufeng(College of Mechanical ＆ Power Engineering, China Three Gorges Univ. , Yiehang 443002, Chin)
出处
《三峡大学学报（自然科学版）》
CAS
2017年第3期84-88,共5页
Journal of China Three Gorges University：Natural Sciences
基金
湖北省自然科学基金(ZRY2014001091)
关键词
柔性作业车间调度问题
双层编码
混合元胞粒子群算法
flexible job shop scheduling problem
double-layer coding
cellular particle swarm optimization algorithm
