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考虑多编组站转场作业的铁路枢纽车流组织优化

李冰 任泽强 轩华

李冰, 任泽强, 轩华. 考虑多编组站转场作业的铁路枢纽车流组织优化[J]. 西南交通大学学报, 2023, 58(3): 489-498, 545. doi: 10.3969/j.issn.0258-2724.20210796
引用本文: 李冰, 任泽强, 轩华. 考虑多编组站转场作业的铁路枢纽车流组织优化[J]. 西南交通大学学报, 2023, 58(3): 489-498, 545. doi: 10.3969/j.issn.0258-2724.20210796
LI Bing, REN Zeqiang, XUAN Hua. Optimization of Wagon Flow Assignment with Transship Work for Multiple Marshaling Stations at Railroad Terminals[J]. Journal of Southwest Jiaotong University, 2023, 58(3): 489-498, 545. doi: 10.3969/j.issn.0258-2724.20210796
Citation: LI Bing, REN Zeqiang, XUAN Hua. Optimization of Wagon Flow Assignment with Transship Work for Multiple Marshaling Stations at Railroad Terminals[J]. Journal of Southwest Jiaotong University, 2023, 58(3): 489-498, 545. doi: 10.3969/j.issn.0258-2724.20210796

考虑多编组站转场作业的铁路枢纽车流组织优化

doi: 10.3969/j.issn.0258-2724.20210796
基金项目: 国家自然科学基金(U1604150,U1804151);河南省科技攻关计划项目(232102321026,232102321093);河南省教育厅哲学社会科学应用研究重大项目(2022-YYZD-24)
详细信息
    作者简介:

    李冰(1976—),男,教授,博士,研究方向为运输组织优化,E-mail:lbing@zzu.edu.cn

  • 中图分类号: U291; U294; N945

Optimization of Wagon Flow Assignment with Transship Work for Multiple Marshaling Stations at Railroad Terminals

  • 摘要:

    为提升多编组站铁路枢纽车流运转效率,首先,在分析枢纽内不同类型车流作业过程的基础上,以枢纽内列车进出站走行、车流改编、列车集结和转场扰动惩罚费用最小化为目标,构建数学模型;其次,根据接入枢纽铁路方向和场站接发列车能力,生成列车-编组站-调车系统初始匹配方案,进而利用转场-解集编能力对匹配方案进行调整与检验;再次,给出列车-编组站-调车系统匹配方案的双层编码方案,并利用嵌入替换-扰动-交叉操作的异步循环更新过程,完成匹配方案的群体寻优;最后,设计仿真试验对所提模型与算法进行测试. 结果表明:所提方法能够完成对多编组站的合理分工,5组试验测试分别在369、422、516、641、763 s的计算时间内得到列车-编组站-调车系统匹配方案,在优化分工后的车流组织方案中,转场货车占到解车流比例均能稳定在20%,从而达到减少转场货车数、降低车流组织成本的目的.

     

  • 图 1  多编组站铁路枢纽列流-车流组织过程

    Figure 1.  Operation process of train flow–wagon flow in railroad terminal with multiple marshaling stations

    图 2  到解列车-编组站接入方案生成示意

    Figure 2.  Illustration of generating matching scheme between arrival train and marshalling station

    图 3  HHTS算法收敛曲线

    Figure 3.  Convergence curve of HHTS algorithm

    图 4  5种不同规模试验场景下的求解结果比对

    Figure 4.  Comparison of solution results under five test scenarios of different scales

    表  1  编组站间距离及衔接方向

    Table  1.   Traveling distance between multiple marshaling stations and railway direction connecting marshaling stations

    编组站编组站间距离/km衔接方向
    m1m2m3
    m1036.444.61,2,3,4,5
    m236.4031.61,2,3,4,6
    m344.631.601,2,3,6
    下载: 导出CSV

    表  2  枢纽内编组站与枢纽外相邻技术站间走行距离

    Table  2.   Traveling distance between marshalling stations inside terminal and adjacent technical stations outside terminal km

    方向 编组站衔接方向
    123456
    进入
    枢纽
    m13.836.238.73.615.516.6
    m233.24.827.429.417.214.5
    m341.531.74.438.639.342.1
    离开
    枢纽
    m14.234.644.55.314.915.3
    m229.66.723.830.616.613.2
    m341.230.45.242.143.445.3
    下载: 导出CSV

    表  3  编组站作业能力

    Table  3.   Technical operation capability of marshalling stations 车/d

    编组站调车
    系统
    技术作业能力
    到达解体集结编组出发
    m1k0700850800750650
    k1650800750750700
    m2k0250400450450300
    k1250450400350300
    m3k0350500450450300
    k1300350350400350
    下载: 导出CSV

    表  4  编组站作业费用

    Table  4.   Operation cost of marshalling stations

    编组站顺向改编/
    (元•车−1
    集结消耗/
    (元•车−1•h−1
    转场费用/(元•车−1
    m1m2m3
    m11.020.162.052.222.18
    m21.090.182.222.042.21
    m31.080.172.182.212.12
    下载: 导出CSV

    表  5  枢纽各衔接方向接发列车信息

    Table  5.   Data of arrival and departure trains from different railway directions

    列车
    类型
    衔接方向
    123456
    到解列车a1a15a16a21a22a25a26a28a29a31a32a33
    始发列车 d1d4d5d14d15d20d21d23d24d25d26
    直通接入 e1e3e4e5 e6e7
    直通编发e4e1e2e3e6e5e7
    下载: 导出CSV

    表  8  列车-编组站-调车系统匹配方案

    Table  8.   Matching scheme of train–marshaling station–shunting system

    列车匹配方案列车匹配方案列车匹配方案
    a1m1, k0a23m3, k1d12m1, k1
    a2m1, k0a24m1, k1d13m2, k1
    a3m1, k0a25m1, k1d14m1, k1
    a4m1, k0a26m2, k0d15m1, k1
    a5m1, k0a27m2, k0d16m1, k0
    a6m1, k1a28m2, k0d17m3, k1
    a7m1, k1a29m1, k0d18m1, k0
    a8m1, k1a30m1, k1d19m1, k0
    a9m1, k0a31m1, k0d20m1, k0
    a10m1, k0a32m3, k1d21m1, k1
    a11m3, k0a33m2, k1d22m2, k1
    a12m3, k0d1m1, k0d23m1, k1
    a13m2, k1d2m1, k0d24m1, k1
    a14m1, k1d3m1, k0d25m1, k0
    a15m3, k1d4m3, k1d26m2, k0
    a16m1, k0d5m1, k0e1m1, k0
    a17m1, k0d6m1, k0e2m1, k0
    a18m1, k0d7m1, k0e3m1, k1
    a19m1, k0d8m2, k0e4m2, k0
    a20m1, k0d9m3, k0e5m2, k1
    a21m1, k0d10m1, k0e6m1, k0
    a22m3, k0d11m3, k0e7m3, k0
    下载: 导出CSV

    表  6  到解列车车流结构信息表

    Table  6.   Data of wagon groups in arrival train flows

    列车车组编号货车数/车列车车组编号货车数/车
    a142-43-445-20-25a1826-27-2822-14-14
    a245-46-473-20-26a1929-30-3111-23-16
    a348-49-503-22-25a200-32-3324-20-6
    a40-51-524-22-24a210-34-3516-22-12
    a50-53-544-21-25a220-1235-15
    a60-55-566-21-26a2313-14-1516-24-10
    a70-57-583-20-24a240-16-17-1811-10-15-14
    a859-6024-26a250-1943-7
    a90-61-623-22-25a260-36-3710-12-28
    a100-6322-28a270-38-3912-14-22
    a110-6423-27a280-40-415-17-28
    a120-65-6610-17-23a290-1-219-20-21
    a13050a300-3-415-10-25
    a14050a310-5-6-726-3-10-11
    a15050a320-826-23
    a1620-21-2220-13-17a330-9-10-1116-6-11-15
    a170-23-24-257-10-21-12
    注:货车数与车组编号相互对应,例如:列车a1由42号车组(5车)、43号车组(20车)和44号车组(25车)构成,表7同.
    下载: 导出CSV

    表  7  始发列车车流结构信息表

    Table  7.   Data of wagon groups in departure train flows

    列车车组编号货车数/车列车车组编号货车数/车
    d10-34-355-22-23d151-3-5720-10-20
    d20-5-24-264-3-21-22d1643-46-620-20-10
    d30-20-3210-20-20d178-21-2723-13-14
    d40-1426-24d180-49-516-22-22
    d544-5025/25d190-53-558-21-21
    d647-5226-24d200-59-614-24-22
    d754-6225-25d2128-31-33-3516-16-6-12
    d837-3928-22d220-22-2521-17-12
    d 964-6627-23d230-4-1120-25-15
    d102-7-6311-11-28d2465-13-16-1917-16-10-7
    d1110-12-15-1813-15-10-14d250-9-29-7-6-11-
    d1256-5826-2436-3812-14
    d130-4122-28d2617-40-23-15-17-7-
    d140-6024-2642-45-485-3-3
    下载: 导出CSV

    表  9  5种不同规模试验场景下的求解结果评测

    Table  9.   Evaluation of solution results under five test scenarios of different scales

    场景运行时间/s车流组织
    成本/元
    顺向日均改编车
    货数/(车•d−1
    同编组站不同调车系统间日均转场货车数/(车•d−1不同编组站间日均转场货车数/
    (车•d−1
    转场货车占到解车流比例/%
    场景Ⅰ369110701261 4834123.6
    场景Ⅱ 422 13276 1864 124 412 22.3
    场景Ⅲ 516 19377 2601 238 461 21.2
    场景Ⅳ 641 25683 3186 265 549 20.5
    场景Ⅴ76333126392629872620.7
    下载: 导出CSV

    表  10  5种不同规模试验场景下的算法性能比对

    Table  10.   Performance comparison of algorithms under five test scenarios of different scales

    试验场景车流组织成本/元CPU 运行时间/s改进率/%
    MTY-GAMTY-AFSAHHTSMTY-GAMTY-AFSAHHTS12
    场景Ⅰ11697.211553.211120.2196.2307.6373.2 5.2 3.9
    场景Ⅱ14236.113996.113292.1213.3368.1415.3 7.1 5.3
    场景Ⅲ21859.320507.219304.2248.8445.7525.713.26.2
    场景Ⅳ28683.427783.425732.4287.6547.8634.511.58.0
    场景Ⅴ37636.536471.933107.7324.3636.4769.813.710.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-18
  • 修回日期:  2022-05-07
  • 网络出版日期:  2023-03-09
  • 刊出日期:  2022-05-26

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