地铁-货车联运的两阶段物流节点选址研究  

Two-stage logistics node location selection study for metro-truck intermodal transportation

作　　者：孙颖杰 吴芳[1] 马军平 SUN Yingjie;WU Fang;MA Junping(School of Transportataion,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学交通运输学院,兰州730070

出　　处：《交通科技与经济》2025年第2期1-8,共8页Technology & Economy in Areas of Communications

基　　金：国家自然科学基金项目(42364003)。

摘　　要：推行基于地铁-货车联运配送的城市物流新型配送模式,是应对传统货车配送引起的市中心交通拥堵和环境污染问题的有效途径。物流节点选址问题是该配送模式中重要的决策问题,构建两阶段地铁-货车联运的物流节点选址模型进行物流节点选址:第一阶段,选择郊区地铁始发站作为备选地铁配送站,利用三角模糊数对地铁配送站进行方案比选;第二阶段,基于第一阶段比选结果,构建总成本最小化的地铁转运站选址模型,并用Python调用COPT求解器求解。以上海市地铁网络为例验证模型和算法,并进行敏感度分析。结果表明,Python调用COPT求解器求解选址分配问题展现出卓越的计算效能,综合考虑末端配送模式、开放地铁转运站数和地铁转运站最大服务范围可获得效益最大化的选址方案。The new urban logistics distribution model based on metro-truck intermodal transportation effectively addresses downtown traffic congestion and environmental pollution caused by traditional truck delivery.The logistics node selection is a crucial decision-making issue in this distribution model.A two-stage metro-truck intermodal transportation logistics node location selection model is constructed.In the first stage,suburban subway starting stations are selected as alternative subway distribution stations,using triangular fuzzy numbers for comparison.In the second stage,based on the results of the first stage comparison,a subway transfer station selection model minimizing total costs is built,and the COPT solver in Python is utilized for optimization.The model and algorithm are validated and sensitivity analysis is conducted using the Shanghai subway network as an example.The results show that Python invoking the COPT solver demonstrates outstanding computational efficiency in solving location allocation problems.Maximizing benefits can be achieved by considering the end delivery mode,the number of open subway transfer stations,and the maximum service range of subway transfer stations.

关 键 词：地铁货运 选址问题 三角模糊数 COPT求解器 敏感度分析 

分 类 号：U121[交通运输工程]