计算机应用与软件2018，Vol.35Issue(3)：49-53,74,6.DOI:10.3969/j.issn.1000-386x.2018.03.009
改进的粒子群算法在多目标车间调度的应用
APPLICATION OF IMPROVED PARTICLE SWARM OPTIMIZATION IN MULTI-TARGET WORKING WORKSHOP SCHEDULING
摘要
Abstract
Using particle swarm algorithm to solve the problem of workshop scheduling is a kind of effective strategy. In this paper, the particle swarm optimization algorithm was analyzed.Aiming at the multi-objective flexible shop scheduling problem,a multi-objective flexible shop scheduling model was constructed, which minimized the processing time,minimized the processing cost and minimized the maximum single machine load.A particle swarm optimization algorithm based on crossover mutation was proposed to improve its ability to jump out of local optimum and reach the global optimum.At the same time,the concept of smart car was introduced to consider the transportation time into this scheduling.The method was applied to the job shop scheduling of a flexible manufacturing plant in a discrete manufacturing industry.Finally,the practicability and efficiency of this algorithm were verified.关键词
柔性车间调度/多目标/粒子群算法/运输时间Key words
Flexible job shop scheduling/Multi-objective/Particle swarm algorithm/Transportation time分类
信息技术与安全科学引用本文复制引用
李浩,毕利,靳彬锋..改进的粒子群算法在多目标车间调度的应用[J].计算机应用与软件,2018,35(3):49-53,74,6.基金项目
国家自然科学基金项目(61662058). （61662058）
