基于改进粒子群算法的烟草物流优化方案  

Tobacco Logistics Network Information Optimization Based on Improved Particle Swarm Algorithm

作　　者：程倩[1] 

机构地区：[1]桐城师范高等专科学校,安徽桐城231400

出　　处：《兰州工业学院学报》2018年第1期73-77,共5页Journal of Lanzhou Institute of Technology

基　　金：2017年全国高校;职业院校物流教改教研课题(JZW2017002;JZW2017132)

摘　　要：为了减少烟草物流网络成本,设计了基于改进粒子群算法的物流网络优化方案.基于现行的烟草物流网络拓扑结构,在打破行政区域限制的条件下,给出了优化的烟草物流网络拓扑结构;从存储成本、运输成本、服务惩罚成本3个方面考虑,建立了优化后物流网络的数学模型;提出了融合神经网络算法、使用模拟退火控制收敛速度的粒子群算法,将交叉变异引入到粒子群更新,防止其陷入局部最优,使用模拟退火算法控制粒子群收敛速度,并将物流优化方案和改进的粒子群算法应用到广西省烟草物流网络.结果表明,物流成本下降了7.51%,每周节省金额约为$2 100万.To lessen cost of tobacco logistics network, optimized logistics network based on improved particle swarm algorithm is proposed. Based on current tobacco logistics network topology structure, aiming to destroy the limitation of administrative region, and optimized tobacco logistics network topology structure is given. Consider- ing warehouse cost, transportation cost and service punishment cost, mathematic model of optimized logistics net- work is established. Improved particle swarm algorithm is proposed, which is integrated with crossover mutation prevent local optimum, meanwhile, simulated annealing algorithm is used to control rate of convergence. Opti- mized project and improved algorithm are applied to tobacco logistics of Guangxi Province, and it turns out the cost of logistics decreases by 7.51%.

关 键 词：烟草物流 拓扑结构 交叉变异 改进粒子群算法 

分 类 号：F252[经济管理—国民经济]