摘要
在对比传统物流模式与云服务模式下最大覆盖模型区别的基础上,研究由一个供应商、多个配送中心、多个零售商构成的配送网络,构建云服务模式下基于最大覆盖的库存配置-动态选址模型;结合问题特征和约束条件选择有效的遗传算法软色体编码方式,并设置合适的适宜度函数,通过选择、交叉、变异来提高算法性能;通过算例对模型和算法进行验证.研究发现:云服务模式下的最大覆盖模型与传统最大覆盖模型相比能降低成本,且随着参数取值的增加,其成本优势更加明显.
Based on the maximum coverage model comparison between the traditional logistics model and the cloud service mode,we study the distribution network consisting of one supplier,multiple distribution centers,and multiple retailers,and build the inventory allocation-dynamic location model based on maximum coverage under cloud service mode.We select effective genetic algorithm chromosome coding method according to the problem characteristics and constraints,set appropriate fitness function,and improve algorithm performance through selection,crossover and mutation.The model and algorithm are verified through calculation examples.Compared with the traditional maximum coverage model,the maximum coverage model under the cloud service mode can reduce costs,and its cost advantage becomes more obvious as the parameter value increases.
作者
姜燕宁
郝书池
JIANG Yanning;HAO Shuchi(School of Geographical Sciences,Guangzhou University,Guangzhou 510006,China;Department of Commerce,Guangzhou City Polytechnic,Guangzhou 510405,China)
出处
《河南科学》
2020年第5期819-828,共10页
Henan Science
基金
广东省教育厅重点平台及科研项目(2017GWTSCX032)。
关键词
云服务
最大覆盖
库存配置
动态选址
cloud service
maximum coverage
inventory allocation
dynamic location
