摘要
针对现实生产系统中存在的时间参数模糊化问题,给出了一种基于区间值梯形模糊数的模糊柔性车间作业计划问题模型。在对模糊柔性车间作业计划问题进行有效求解方面,针对基本粒子群算法容易陷入局部最优的问题,随后给出了一种基于遗传操作的混合粒子群算法,利用遗传算法思想对粒子进行交叉、变异操作,增强了算法跳出局部最优的能力。仿真实验表明,该算法具有可行性和有效性。
To solve the problems correlated with fuzzy temporal parameter in real manufacture system,this paper introduced a fuzzy flexible job-shop scheduling（ FJSS） model based on interval-valued trapezoidal fuzzy number firstly. After that,aiming at the problems of easily getting into the local optimum of basic particle swarm optimization （ PSO） algorithm,proposed a hybrid PSO algorithm based on crossover and mutation operations of genetic algorithm for the fuzzy FJSS problem above,which helped the algorithm to break away from the local optimum. At last,through the analysis of the simulating experiment results,approved the feasibility and efficency of the algorithm.
出处
《计算机应用研究》
CSCD
北大核心
2010年第10期3721-3723,共3页
Application Research of Computers
基金
江苏省教育厅高校哲学社会科学基金资助项目(09SJD630036)
南京工程学院校级科研基金资助项目(QKJA2009015)
关键词
柔性车间作业计划问题
模糊环境
粒子群算法
遗传算法
flexible job-shop scheduling problem
fuzzy environment
particle swarm optimization
genetic algorithm
