重载铁路混编群组列车开行方案编制模型与算法  

A model and an algorithm for generating mixed group train operation scheme of heavy-haul railway transportation

作　　者：卓芩羽 陈维亚[1,2] 宋宗莹 于晓泉[4] ZHUO Qinyu;CHEN Weiya;SONG Zongying;YU Xiaoquan(School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China;Rail Data Research and Application Key Laboratory of Hunan Province,Changsha 410075,China;The Dispatching and Transportation Department of China Shenhua Energy Co.,Ltd.,Beijing 100011,China;CRSC Research&Design Institute of Signal&Communication Group Co.,Ltd.,Beijing 100070,China)

机构地区：[1]中南大学交通运输工程学院,湖南长沙410075 [2]轨道交通大数据湖南省重点实验室,湖南长沙410075 [3]中国神华能源股份有限公司调运部,北京100011 [4]北京全路通信信号研究设计院集团有限公司,北京100070

出　　处：《铁道科学与工程学报》2025年第2期569-578,共10页Journal of Railway Science and Engineering

基　　金：重大技术开发项目(重载列车群组运行控制系统技术研究与应用,HKF202300450)。

摘　　要：开行群组列车可以缩短列车追踪运行间隔,是提高重载铁路输送能力和减少货物总在途运输时间的潜在突破口。开行混编群组列车有利于灵活编组列车和适应多样化货物运输需求,但会使列车开行方案的编制问题变得复杂。为了优化求解具有“技术站始发直达”特征的重载铁路混编群组列车开行方案(包括混编群组列车的列车组群方案、停站方案和运行时刻方案),本文构建了一个多目标优化模型,并设计了一种启发式求解算法。优化模型引入了货运需求重要度作为参考指标,综合考量货物需求量、运到期限、目的站等级及运输距离等因素,以单位时段内目的站货运供需差额运输成本最小和货物总在途运输时间最短作为优化目标。约束条件主要考虑了货运供需匹配关系、货物运到期限、线路天窗时间、群组内单元列车数量限制等现实运输组织条件。考虑该模型为混合整数非线性规划模型,设计了一种模拟退火非支配排序算法(Simulated Annealing for Non-dominated Sorting, SANSA)进行求解。以某重载铁路为背景构建简化算例,计算结果表明:所构建的多目标优化模型与设计的SANSA算法能够有效获得重载铁路混编群组列车的列车组群方案(包括群组数量、组群顺序、组内单元列车数量)、停站方案和运行时刻方案;在满足既定运输需求计划情形下,该求解结果还可用于反馈分析目的站货运需求计划和最晚运到时间设定的合理性,为运输供给方案的优化调整提供参考依据。The operation of group trains can shorten the train tracking interval,which is a potential breakthrough to improve the carrying capacity of heavy-haul railway transportation and reduce the total transport time of goods.The operation of mixed group trains is conducive to the flexible grouping of trains and the better adaptation of diversified freight transportation needs,but it would make the generation problem of train operation scheme more complicated.A multi-objective optimization model and a heuristic algorithm were proposed to optimize the operation scheme of mixed group trains featured with direct departure from a technical station,including train grouping scheme,stopping scheme and running time scheme.The optimization model introduced the importance of freight demand,which comprehensively considered the demand of goods,delivery time,destination station grade and transportation distance as reference indicators,and took the minimum transportation cost and the shortest total transport time of goods as optimization objectives.The constraint conditions were mainly extracted from the actual transportation organization conditions such as the matching relationship between freight supply and demand,the delivery time of goods,the skylight time of the heavy-haul railway line,and the limitation of the maximum number of unit trains in a group.A Simulated Annealing for Non-dominated Sorting(SANSA)algorithm was designed to solve the proposed mixed integer nonlinear programming model.A simplified calculation example was constructed based on a heavy-haul railway line for coal transportation in northwestern China.The calculation results show that the proposed multi-objective optimization model and the SANSA algorithm can effectively generate the train grouping scheme,i.e.,the number of train groups,group orders,the number of unit trains in a group,the stopping scheme and the running time scheme.In the case of satisfying the planned transportation demand scheme,the solution results can also be used as the feedback to analyze

关 键 词：重载铁路运输 混编群组列车 开行方案 多目标优化 元启发式算法 

分 类 号：U296[交通运输工程—交通运输规划与管理]