摘要
针对电商仓配模式中仓储爆仓、配送延迟和配送成本高等不足,提出基于轴辐式网络的电商云仓配送优化方案。以各运营主体的收益最大化为目标函数建立云仓轴辐式网络决策分析模型,并基于编码方式不同设计两阶段遗传算法求解模型。构造算例验证模型和算法,设置两阶段遗传算法与单一遗传算法、基础轴辐式网络与云仓轴辐式网络两组对照实验。结果表明:两阶段遗传算法较单一遗传算法对于投资回报率的提升为136.7%;建立云仓之后,轴辐式网络的投资回报率从178.5%提升至213.3%,证明了提出的模型和算法对于解决轴辐式网络优化问题的有效性,为未来电商物流的转型升级与降本增效提供了可行方向。
Addressing the shortcomings of warehouse overload,delivery delays,and high delivery costs in the e-commerce warehouse distribution model,a hub-and-spoke network-based optimization solution for e-commerce cloud warehouse distribution was proposed.A decision analysis model for the cloud warehouse hub-and-spoke network was established,with the objective function of maximizing the revenue of each operating entity.Additionally,a two-stage genetic algorithm was designed to solve the model based on different encoding methods.A case study was constructed to validate the model and algorithm,and two control experiments were set up:comparing the two-stage genetic algorithm with the single genetic algorithm,and the basic hub-and-spoke network with the cloud warehouse hub-and-spoke network.The results indicated that the two-stage genetic algorithm improved the return on investment by 136.7%compared to the single genetic algorithm.After establishing the cloud warehouse,the investment return rate of the hub-and-spoke network increased from 178.5%to 213.3%,proving the effectiveness of the proposed model and algorithm in solving the optimization problem of the hub-and-spoke network.This provides a feasible direction for the transformation,upgrading,cost reduction,and efficiency improvement of future e-commerce logistics.
作者
万迎希
胡志华
包晓琼
WAN Yingxi;HU Zhihua;BAO Xiaoqiong(Institute of Logistics Science and Engineering,Shanghai Maritime University,Shanghai 201306,China;不详)
出处
《武汉理工大学学报（信息与管理工程版）》
CAS
2024年第5期727-733,共7页
Journal of Wuhan University of Technology：Information & Management Engineering
基金
国家自然科学基金面上项目(71871136)。
关键词
云仓仓配
轴辐式网络
电子商务
混合整数规划
物流管理
cloud warehouse distribution
hub-and-spoke network
e-commerce
mixed integer programming
logistics management
