High-speed Railway Express Cargo Flow Allocation and Operation
Organization Optimization Under Varying Demand

doi:10.16097/j.cnki.1009-6744.2022.05.018

Journal of Transportation Systems Engineering and Information Technology ›› 2022, Vol. 22 ›› Issue (5): 174-186.DOI: 10.16097/j.cnki.1009-6744.2022.05.018

Previous Articles Next Articles

High-speed Railway Express Cargo Flow Allocation and Operation Organization Optimization Under Varying Demand

CHEN Xing-han^{1a, 1b} , ZHOU Pei-yu^1a , LANG Mao-xiang^{* 1a, 1b} , YU Xue-qiao ² , LI Shi-qi ^1a

1a. School of Traffic and Transportation, 1b. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 2. Transportation and Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China

Received:2022-05-22 Revised:2022-07-08 Accepted:2022-07-11 Online:2022-10-25 Published:2022-10-22
Supported by:
Joint Funds of the National Natural Science Foundation of China (U2034208)；National Key Research and Development Program of China (2021YFB1407003)；Fundamental Research Funds for the Central Universities (2021YJS097)。

需求变动下高铁快运货流分配与组织模式优化

陈星瀚^{1a, 1b} ，周培宇^1a ，郎茂祥^{* 1a, 1b} ，于雪峤²，李世琦^1a

1. 北京交通大学，a. 交通运输学院，b. 综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044； 2. 中国铁道科学研究院集团有限公司，运输及经济研究所，北京 100081

作者简介:陈星瀚(1993- )，男，天津人，博士生。
基金资助:
国家自然科学基金联合基金；国家重点研发计划；中央高校基本科研业务费专项资金。

Abstract

Abstract: This paper proposes the optimization method for cargo flow allocation and organization under the normalization and large-scale network operation for the high-speed railway (HSR) express considering the operational characteristics of the HSR express with one place cargo collection and multiple places for shipment and the variation of the demand of China's express market in the future. The main decision variables are the volume of HSR express between each Origin Destination (OD) pair under the mode of passenger train piggyback transport, reserved carriage and HSR freight train. The objective is to minimize the total operating costs. A mixed- integer programming (MIP) model is proposed for cargo flow allocation, which calculates the allocation of cargo flow in the HSR express network and creates the combination optimization scheme of cargo flow OD transport route and the operation organization mode for each HSR express channel. The method of "three-stage" forecasting of HSR express cargo flow OD is used to forecast the demand for HSR express between cities as the cargo flow input. The numerical experiments based on a network consisting of nine domestic express central cities are conducted to verify the effectiveness of the proposed methods, which are all computed by the Gurobi solver code in the Python programming language. The results show that using the piggyback and reserved modes in the low volume period can accommodate 90.7% of the transport needs. In the peak period, the operation organization mode can be adjusted according to the adaptive relationship between OD pairs of cargo flow and network capacity so as to fully utilize the idle capacity of HSR and improve operational efficiency.

Key words: railway transportation, cargo flow allocation, mixed-integer programming, high-speed railway express; operation organization mode

摘要： 基于高铁快运一地集货和多点发运的开行特点，综合考虑我国未来快递市场的需求变动，提出高铁快运常态化和规模化成网运营后的货流分配与组织模式优化方法，以客运动车组捎带运输货物、预留车厢和高铁快运专列模式下，各OD对间的高铁快运运量为主要决策变量，以总运输成本极小化为目标，构建货流分配混合整数规划模型，计算货流量在高铁快运网络中的分布，得到货流OD运输路线与各高铁快运通道运营组织模式的组合优化方案。通过高铁快运货流OD “三阶段”预测方法，预测城市间的高铁快运需求量作为货流输入，以9个国内快递业务中心城市组成的网络为案例，由Python语言调用Gurobi优化软件求解案例，验证所建模型的有效性。案例结果表明：在运量低谷期使用捎带和预留模式可满足90.7%的运输需求；在运量高峰期部分线路需开行高铁快运专列；运营组织模式可根据OD对间货流量与网络容量的适应性关系进行调整，以充分利用高铁闲置运力，提升运营效益。

关键词: 铁路运输, 货流分配, 混合整数规划, 高铁快运, 运营组织模式

CLC Number:

U116.5

CHEN Xing-han, ZHOU Pei-yu, LANG Mao-xiang, YU Xue-qiao, LI Shi-qi . High-speed Railway Express Cargo Flow Allocation and Operation Organization Optimization Under Varying Demand[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 174-186.

陈星瀚, 周培宇, 郎茂祥, 于雪峤, 李世琦. 需求变动下高铁快运货流分配与组织模式优化[J]. 交通运输系统工程与信息, 2022, 22(5): 174-186.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.05.018

http://www.tseit.org.cn/EN/Y2022/V22/I5/174

References

[1] 何世伟, 郎茂祥, 魏玉光. 高铁货运具有巨大发展潜能 [N]. 北京: 中国交通报, 2020-11-04(3). [HE S W, LANG M X, WEI Y G. High-speed rail freight has great potential for development[N]. Beijing: China Transport News, 2020-11-04(3).]

[2] BI M K, HE S W, XU W T. Express delivery with highspeed railway: Definitely feasible or just a publicity stunt [J]. Transportation Research Part A: Policy and Practice, 2019, 120: 165-187.

[3] DELGADO F, TRINCADO R, PAGNONCELLI B K. A multistage stochastic programming model for the network air cargo allocation under capacity uncertainty[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 131: 292-307.

[4] 于雪峤, 郎茂祥, 周凌云, 等. 高铁快运产品货流分担率计算方法研究[J]. 交通运输系统工程与信息, 2019, 19(1): 40-44, 54. [YU X Q, LANG M X, ZHOU L Y, et al. Calculation method of the freight flow sharing rate of high-speed railway express products[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(1): 40-44, 54.]

[5] 李辰中, 孟凡栋, 周凌云. 我国高铁快运业务运营组织模式探讨[J]. 铁道运输与经济, 2019, 41(3): 48-52, 74. [LI C Z, MENG F D, ZHOU L Y. A tentative study on the operation organization mode of China's high-speed railway express business[J]. Railway Transport and Economy, 2019, 41(3): 48-52, 74.]

[6] 戚建国, 周厚盛, 杨立兴, 等. 灵活编组条件下轨道交通客货协同运输方案优化[J]. 交通运输系统工程与信息, 2022, 22(2): 197-205. [QI J G, ZHOU H S, YANG L X, et al. Optimization methods of combined passenger and freight transportation based on flexible train composition mode[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 197-205.]

[7] 田亚明, 林柏梁, 纪丽君. 基于多商品流和虚拟弧的铁路车流分配点-弧、弧-路模型研究[J]. 铁道学报, 2011, 33(4): 7-12. [TIAN Y M, LIN B L, JI L J. Railway car flow distribution node-arc and arc-path models based on multi-commodity and virtual arc[J]. Journal of the China Railway Society, 2011, 33(4): 7-12.]

[8] 丁小东. 我国快递网络“三阶段法”流量分析模型研究 [J]. 铁道运输与经济, 2019, 41(3): 53-57. [DING X D. A research on "Three-stage method" traffic analysis model of express network in China[J]. Railway Transport and Economy, 2019, 41(3): 53-57.]

[9] 王莹, 韩宝明, 张琦, 等. 基于 SARIMA 模型的北京地铁进站客流量预测[J]. 交通运输系统工程与信息, 2015, 15(6): 205- 211. [WANG Y, HAN B M, ZHANG Q. Forecasting of entering passenger flow volume in Beijing subway based on SARIMA model[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 205-211.]

[10] 董春娇, 邵春福, 熊志华, 等. 基于Elman神经网络的道路网短时交通流预测方法[J]. 交通运输系统工程与信息, 2010, 10(1): 145-151. [DONG C J, SHAO C F, XIONG Z H. Short-term traffic flow forecasting of road network based on Elman neural network[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(1): 145-151.]

[11] DWANG C H. Considering economic indicators and dynamic channel interactions to conduct sales forecasting and for retail sectors[J]. Computers & Industrial Engineering, 2022, 165: 107965.

[12] 李林汉, 岳一飞, 田卫民. 基于PCA与Markov残差灰色模型的京津冀物流能力评价和预测[J]. 北京交通大学学报(社会科学版), 2019, 18(2): 129-142. [LI L H, YUE Y F, TIAN W M. Principal component analysis and Markov residual grey based evaluation and prediction of logistics capabilities of Beijing, Tianjin and Hebei[J]. Journal of Beijing Jiaotong University (Social Sciences Edition), 2019, 18(2): 129-142.]

[13] SHEU J B, KUNDU T. Forecasting time-varying logistics distribution flows in the One Belt-One Road strategic context[J]. Transportation Research Part E: Logistics and Transportation Review, 2018, 117: 5-22.

[14] 张程. 中国快递运输量与货品价值测算[J]. 长安大学学报(社会科学版), 2015, 17(3): 20-23. [ZHANG C. Estimation of industry transportation volume and value of national courier industry[J]. Journal of Chang'an University (Social Science Edition), 2015, 17(3): 20-23.

[1]	FENG Fen-ling, CAI Ming-xu, JIA Jun-jie. Key Node Identification of China Railway Express Transportation Network Based on Multi-layer Complex Network [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 191-200.
[2]	ZHANG Hai, LV Miao-miao, NI Shao-quan. Train Timetable Optimization Model for Full-length and Short-turn Routings with Irregular Departure Intervals [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 224-233.
[3]	WANG Zhi-mei, YAO Shang-jun, CUI Zan-yang. Joint Optimization of Car Routing and Train Formation Plan Based on Transfer of Freight Flow from Road to Railway [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 19-25.
[4]	YAN Zhen-ying, WANG Yu, HAN Bao-ming, LI Xiao-juan. Joint Optimization of Ticket Price and Ticket Time Window for High-speed Railway Based on Preference Order Choice [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 164-173.
[5]	JI Jin-hua, LIU Ya-jun, BIE Yi-ming, WANG Lin-hong. Delivery Method of Living Goods in Controlled Communities Based on Cooperation Between Drones and Truck [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 264-272.
[6]	YANG Yun , ZHANG Xiao-qiang, XU Xin-hao. Intercity Train Time-sharing Pricing Based on Cumulative Prospect Theory [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 23-29.
[7]	WANG Yu , MIAO Lei. Differential Pricing Optimization of High-speed Railways Based on Discrete Price [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 7-14.
[8]	GENG Qing-qiao , JIA Yuan-hua , CHEN Jun-tuan. Risk Decision of Railway "Go Global" Public-private Partnership Project Based on Variable Weight Extension Matter Element [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 64-71.
[9]	QI Jian-guo , ZHOU Hou-sheng, YANG Li-xing, ZHOU Ya-ru, ZHANG Chun-tian. Optimization Methods of Combined Passenger and Freight Transportation Based on Flexible Train Composition Mode [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 197-205.
[10]	ZHANG Xiao-ming , XU Fang , JIANG Wen-hui , JIANG Chao-zhe. Pricing and Ordering Decisions of China Railway Express Considering Capital Constraint [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 30-36.
[11]	ZHANG Chun-tian , QI Jian-guo , YANG Li-xing , GAO Yuan , GAO Zi-you. Robust Train Operation Plan Based on Uncertain Passenger Demands for High-speed Railway Corridors [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 115-123.
[12]	ZHOU Yong , ZHANG Jie , ZHONG Ling-chong, LI Wen-feng , WANG Guo-dong. Flexible Cooperative Scheduling Optimization of Multiple Rail Mounted Gantry Cranes in Railway Container Terminals [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 133-141.
[13]	NIU Hui-min. Literature Review on Rail Train Timetabling [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 114-124.
[14]	LIU Ge-hui, CHEN Shao-kuan, LIU Shuang, JIN Hua, WANG Dan-yang. Maintenance Arrangement Optimization for Infrastructure Systems of Urban Rail Transit Considering Resource Constraints [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 163-169.
[15]	XU Zhi-yong. Collaborative Optimization on Electrical Resolver Location and Track Gradient Design of Railway [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 170-175.

High-speed Railway Express Cargo Flow Allocation and Operation Organization Optimization Under Varying Demand

需求变动下高铁快运货流分配与组织模式优化

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics