首页|旅客列车捎带模式下的高铁快运网络配装方案优化

旅客列车捎带模式下的高铁快运网络配装方案优化

Demand assignment for express delivery in high-speed railway networks:A passenger train delivery mode

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
针对高铁快运网络货物运输配装优化问题,本文提出既有旅客列车捎带模式下的货物运输两阶段配装方案编制方法.第一阶段基于高铁快运网络采用K短路径算法计算货运需求可行路径集;第二阶段结合需求运输路径类型、列车装载状态以及列车跨线运行情况,构建运输配装方案优化模型.此外,由于组合优化问题的复杂性以及模型非线性和多变量特点,传统粒子群算法求解不同货运规模下的运输配装方案模型效率较低.因此,本文通过循环迭代优化运输路径和货物配装方案,提出改进的嵌套粒子群算法以提高求解效率和精度.最后,本文以哈大客专、京沈客专、长白乌等10条高速铁路及动车组运行线路构成的路网为例,验证运输配装方案模型和算法的有效性.案例结果表明,考虑开行跨线列车的运输配装方案优化模型减少了约5％的需求滞留量.此外,嵌套粒子群算法能够有效求解大规模高铁网络货物运输配装问题,在求解效率上较经典算法提升约20％.
Aiming at the optimization of demand transportation and assignment in the express delivery network with high-speed railway,this paper proposes a two-stage demand assignment method under the passenger train delivery mode.In the first stage,we use the K-shortest al-gorithm to calculate the set of feasible transportation routes for freight demands.In the second stage,an optimization model for network train demand assignment plan is built by introducing the type of demand transportation path,train loading state and train cross line operation.In addition,due to the complexity of combinatorial optimization problems and the nonlinear and multivariable characteristics of the model,the traditional particle swarm optimization algorithm has low efficiency in solving transportation and assignment plan models under different freight scales.Therefore,this paper proposes an improved nested particle swarm optimization algorithm to improve the efficiency and accuracy of the solution by iteratively optimizing the transportation path and demand assignment plan.Finally,the paper takes the high-speed railway and EMU operating network composed of 10 lines,including Harbin Dalian Railway,Beijing Shenyang Railway,Changchun Baicheng Wulumuqi Railway,etc.,as an example to verify the demand assignment model and the effectiveness of the algorithm.The experimental results indicate that the optimization model for the transportation and assignment plan considering the operation of cross-line trains reduces the demand backlog by 5％approximately.In addition,the nested particle swarm optimization algorithm can effectively solve the demand transportation and as-signment problem for large-scale high-speed railway networks,and the efficiency of the solution is improved by about 20％compared with the classical algorithm.

express delivery with high-speed railwaytransportation routedemand assignment modeltwo-phase algorithmnested particle swarm optimization

杨喜梅、康柳江、孙会君、吴建军

展开 >

北京交通大学综合交通运输理论交通运输行业重点实验室,北京 100044

北京交通大学系统科学学院,北京 100044

高铁快运 运输路径 配装模型 两阶段算法 嵌套粒子群

国家自然科学基金国家自然科学基金国家自然科学基金北京交通大学人才基金

7228810172361137003723310012023XKRC036

2024

10.12011/SETP2023-0119

系统工程理论与实践
中国系统工程学会

系统工程理论与实践

CSTPCDCSSCI北大核心
影响因子：1.575
ISSN：1000-6788
年,卷(期)：2024.44(5)
  • 6