地铁-货车联运的地铁转运站选址算法比较  

Comparison of Metro Transfer Station Siting Algorithms for Metro-Truck Intermodal Transportation

作　　者：孙颖杰 吴芳[1] 刘亚丽 SUN Yingjie;WU Fang;LIU Yali(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,甘肃兰州730070

出　　处：《物流科技》2025年第2期28-32,共5页Logistics Sci Tech

基　　金：国家自然科学基金项目(42364003)。

摘　　要：针对传统算法解决复杂非线性规划收敛速度慢、寻优精确度低等问题,文章介绍并设计了模拟退火算法、自适应免疫遗传算法以及Python调用COPT求解器三种算法对地铁-货车联运的地铁转运站选址问题进行求解。最后,以西安市地铁网络为例,分别运用这三种算法对地铁转运站选址问题进行求解,并对求解结果进行比较分析。结果表明,Python调用COPT求解器的算法在解决地铁转运站选址问题时,相较于自适应免疫遗传算法和模拟退火算法有着卓越的计算效能和精确度。Aiming at problems such as slow convergence speed and low accuracy of optimization when using the traditional algorithms to solve the problems of complex nonlinear planning,this paper introduces and designs three algorithms,namely,simulated annealing algorithm,adaptive immune genetic algorithm,and Python calling the COPT solver,for solving the metro-truck intermodal transportation metro transfer station siting problem.Finally,taking Xi'an metro network as an example,these three algorithms are applied to solve the metro transfer station siting problem,and the results are compared and analyzed.The results show that the Python algorithm calling the COPT solver has superior computational efficiency and accuracy compared with the adaptive immune genetic algorithm and simulated annealing algorithm in solving the metro transfer station siting problem.

关 键 词：地铁货运 选址问题 COPT求解器 自适应免疫遗传算法 模拟退火算法 

分 类 号：F572[经济管理—产业经济]