摘要
首先针对多项目多资源均衡问题的特点,建立描述问题的多目标优化模型;然后将Pareto方法嵌入向量评价微粒群算法(VEPSO),提出一种新的基于Pareto的向量评价微粒群算法(VEPSO-BP);最后利用一个算例测试了VEPSO-BP的性能,并与VEPSO进行了对比.实验结果表明,VEPSO-BP的收敛性能优于VEPSO,实现了对多项目多资源均衡问题的高质量求解.
Based on the characteristics of multi-resource leveling in multiple projects scheduling problem,a multi-objectives optimization model is setup.By applying Pareto optimal method into vector evaluated particle swarm optimization（VEPSO）, a new vector evaluated particle swarm optimization based on Pareto（VEPSO-BP） is proposed.Finally,the performance of VEPSO-BP is tested with a testing example which is compared with VEPSO.Experiment results show that,VEPSO-BP is better than VEPSO in convergence efficiency,which also performs well in solving multi-resource leveling in multiple projects scheduling problem.
出处
《控制与决策》
EI
CSCD
北大核心
2010年第5期789-793,共5页
Control and Decision
关键词
微粒群算法
PARETO最优
资源均衡
多项目管理
Particle swarm optimization
Pareto optimal
Resource leveling
Multiple projects management
