摘要
传统的供应链求解方法为投影法,针对其要对投影进行计算,十分复杂的缺点,提出用改进的粒子群算法求解供应链均衡问题,利用动态异步调整学习因子来有效的提高了算法搜索能力与精度.本文介绍了供应链网络均衡问题转变为无约束优化问题的方法,然后用改进的粒子群优化算法进行求解.通过四个数值算例,将实验结果与标准粒子群算法、蜂群算法、学习因子同步变化的粒子群算法进行比较,验证了改进的粒子群优化算法在解决供应链网络均衡问题中的有效性与优越性,为供应链网络求解提供了一种新的方法.
The traditional method of supply chain is projection,but it is very complex to calculate the projection.This paper presents an improved particle swarm algorithm to solve the problem of the supply chain network equilibrium.Constantly adjusting learning factor to balance the global exploring ability and the ability of local development of the algorithm improves optimization precision of algorithm.Through four numerical examples,and comparing with the standard particle swarm algorithm,artificial bee colony algorithm,and the particle swarm optimization algorithm with synchronous learning factor,the improved particle swarm optimization algorithm is proved to be effective and superior in solving the supply chain network equilibrium problem.It provides a new method for solving the supply chain network.
作者
马斌
吴泽忠
MA Bin;WU Ze-zhong(School of Applied Mathematics,Chengdu University of Information Technology,Chengdu 610225,China)
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2020年第2期122-128,共7页
Operations Research and Management Science
基金
国家自然科学基金资助项目(71672013)
四川省科技厅软科学项目(2014ZR0016)
四川省哲学社会科学重点研究基地系统科学与企业研究中心重点项目(Xq14B06)。
关键词
供应链网络
变分不等式
粒子群优化算法
学习因子
supply chains network
variational inequalities
particle swarm optimization
learning factor
