交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (5): 260-271.DOI: 10.16097/j.cnki.1009-6744.2026.05.022

• 系统工程理论与方法 • 上一篇    下一篇

随机需求和运能下高铁快运集货与配装方案协同优化研究

帅斌1a,1b ，徐浩然1c ，谢安昊1c ，孙朝苑*2   

  1. 1.西南交通大学，a.综合交通运输智能化国家地方联合工程实验室，b.综合交通大数据应用技术国家工程实验室， c.交通运输与物流学院，成都611756；2.电子科技大学，经济与管理学院，成都611731
  • 收稿日期:2026-05-18 修回日期:2026-06-15 接受日期:2026-07-10 出版日期:2026-10-25 发布日期:2026-10-21
  • 作者简介:帅斌（1967—），男，四川乐山人，教授，博士。
  • 基金资助:
    国家铁路局项目 (KF2021-29)；中国铁路经济规划研究院有限公司基金课题 (2025YJJ07)。

Integrated Optimization of Consolidation and Demand Assignment for High Speed Railway Express Delivery with Stochastic Demand and Capacity

SHUAI Bin1a,1b, XU Haoran1c, XIE Anhao1c, SUN Chaoyuan*2   

  1. 1a. National United Engineering Laboratory of Integrated and Intelligent Transportation, 1b. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, 1c. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China; 2. School of Economics and Management, University of Electronic Science and Technology of China, Chengdu 611731, China
  • Received:2026-05-18 Revised:2026-06-15 Accepted:2026-07-10 Online:2026-10-25 Published:2026-10-21
  • Supported by:
    Project of the National Railway Administration of China (KF2021-29)；China Railway Economicand Planning Research Institute Co. Ltd. Fund Project (2025YJJ07)。

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摘要： 针对高铁快运需求与可用运能双重不确定性带来的时效与成本权衡难题，本文研究高铁快运枢纽站前端集货环节与列车配装方案的协同优化问题。以不同时效等级货物的集拼发货阈值和预留车厢数量为第1阶段决策变量，以直运与集拼分量和列车配装方案等为第2阶段决策变量，构建考虑库存平衡、阈值触发、运力和卸货能力等约束的两阶段随机规划，最小化期望总成本。通过蒙特卡洛抽样和反向消减法生成场景子集，刻画市场需求与捎带模式可用运能的不确定性，并由Matlab调用Gurobi求解。以成渝贵环线部分线路开展算例验证，结果表明：次日达货物的集拼阈值随运输距离单调递增，次晨达货物阈值除终点站外处于整体偏低的水平，当日达货物呈现近端少远端多的特征，且分别在10、40、50个场景后趋于稳定。预留车厢集中于订单涌入与积压释放的关键车次，占比约23.5%；两阶段随机规划相对期望均值模型的随机解价值稳定在0.69%~1.38%区间，集货机制相比纯直运模式的累计平均节省成本收敛于11000元左右；通过调整集货环节直接相关参数可以实现满载率、延误和弃货率的同步优化。

关键词: 铁路运输, 集货阈值, 两阶段随机规划, 高铁快运, 配装方案, 随机需求

Abstract: High-speed railway express delivery (HSReD) faces a trade-off between timeliness and cost due to dual uncertainties in freight demand and available transport capacity. To address this issue, this paper investigates the integrated optimization of front end freight consolidation process at hub stations and the train loading scheme. A two-stage stochastic programming model is formulated to minimize the expected total cost. In the first stage, the decision variables are the consolidation dispatch thresholds for freight with different timeliness grades and the number of reserved carriages. The second-stage variables include the proportions of direct-shipping and consolidation, as well as the train loading scheme. This model incorporates some constraints such as inventory balance, threshold triggering, train capacity, and station unloading capacity. To characterize the uncertainties of market demand and available piggyback capacity, the model generates a scenario subset by Monte Carlo sampling and the backward reduction method. Then it is solved by using Gurobi via MATLAB. A case study based on the selected sections of the Chengdu-Chongqing-Guiyang railway loop is conducted to verify the model. The results demonstrate that the consolidation threshold for next-day delivery freight increases monotonically with destination distance. The threshold for next-morning delivery freight remains generally low, except at Chengdu East Station. Meanwhile, the threshold for same-day delivery freight is lower for nearby stations and higher for distant stations. These three thresholds stabilize under scenario scales of 10, 40, and 50, respectively. Reserved carriages are concentrated on key train services experiencing order surges and backlog releases, accounting for approximately 23.5%. The value of the stochastic solution for the two-stage model, relative to the expected value model, stabilizes between 0.69% and 1.38%. Compared to a pure direct-shipping mode, the cumulative average cost savings of the consolidation mechanism converge to approximately 11 000 yuan. Finally, by adjusting the fixed consolidation cost and delay penalty coefficient, this paper demonstrates that the consolidation mechanism can simultaneously optimize the train load factor and the freight delay rate.

Key words: railway transportation, consolidation threshold, two-stage stochastic programming, high-speed railway express, demand assignment, stochastic demand

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