摘要
为了对烟草物流配送过程进行优化,以配送物流量的距离最小化、配送时间的方差最小化、不同服务区域之间差异最大化为指标,通过加权糅合构建了物流系统综合优化模型。以聚类算法产生的初始解作为遗传算法初始精英种群,利用配送时间方差的自适应变异算子改进传统遗传算法,设计了基于精英自适应遗传聚类算法,以提高聚类收敛速度,使聚类区域分布更加均衡。以浙江省某市烟草物流配送中心为对象进行测试,结果表明:(1)利用本文算法实现了对应服务区域和中转站的确定以及聚类区域的优化,对3个中转站和1个配送中心模式优化后,零售户直送率提高7.4百分点,按量直送率提高2.9百分点,总里程减少1352 km;(2)优化后万支卷烟物流成本、分拣和仓库费用显著降低,均低于浙江省平均水平;(3)该地区卷烟销量集中区域与采用本文算法规划的配送效果相符合。该方法具有一定的实用性,可为提高物流配送效率提供技术支持。
To optimize tobacco logistics delivery process,by taking the minimization of the distance of delivery amount and the variance of delivery time,and the maximization of the difference between service areas as indexes,an integrated optimization model for logistics system was established via weighting and mixing.Using the solution generated by clustering algorithm as the initial elitist-population in genetic algorithm,and using the self-adaptive mutational operator based on the variance of delivery time to modify the traditional genetic algorithm,a self-adaptive elitist-based genetic clustering algorithm was designed to accelerate the convergence of cluster and even the distribution of cluster areas.The designed algorithm was verified in a city level tobacco logistics delivery center in Zhejiang Province,the results showed that:1)After the optimization of three transit stations and a delivery center,the direct delivery rate to retailers increased by 7.4 percentage points,the direct delivery rate by volume increased by 2.9 percentage points,and total delivery mileage reduced by 1352 km.2)The logistics,sorting and storage costs per10000 cigarettes decreased significantly and were lower than their respective averages in Zhejiang Province.3)The major cigarette markets in the region agreed with the delivery areas planned by the designed algorithm.This method is practicable and provides technology support for promoting the efficiency of logistics delivery.
作者
李存兵
谢林君
杨金欣
LI Cunbing;XIE Linjun;YANG Jinxin(Ningbo Branch of Zhejiang Provincial Tobacco Corporation,Ningbo 315000,Zhejiang,China;Zhejiang University of Technology,Hangzhou 310014,China;Shaoxing Branch of Zhejiang Provincial Tobacco Corporation,Shaoxing 312000,Zhejiang,China)
出处
《烟草科技》
EI
CAS
CSCD
北大核心
2020年第2期94-101,共8页
Tobacco Science & Technology
关键词
烟草物流
配送中心
遗传算法
聚类算法
中转站
自适应变异
Tobacco logistics
Delivery center
Genetic algorithm
Clustering algorithm
Transit station
Self-adaptive mutation
