Research on Optimization of Terminal Cargo Integrated Pickup and Delivery for High-speed Railway Express

被引：0
|
作者：
Li D. [1 ]
Wang L. [1 ,2 ]
Zhu X. [1 ,2 ]
Liu W. [1 ]
Yan W. [3 ]
Li H. [4 ]
机构：
[1] School of Traffic and Transportation, Beijing Jiaotong University, Beijing
[2] Key Laboratory of Transport Industry of Comprehensive Transportation Theory, Beijing Jiaotong University, Beijing
[3] General Office, China State Railway Group Co., Ltd., Beijing
[4] Beijing Branch, China Railway Express Co., Ltd., Beijing
来源：
Tiedao Xuebao/Journal of the China Railway Society | 2024年 / 46卷 / 06期
关键词：
bi-level programming model; high-speed railway express; multi-objective optimization; NSGA- II algorithm; terminal pickup and delivery;
D O I：
10.3969/j.issn.1001-8360.2024.06.002
中图分类号：
学科分类号：
摘要：
With the continuous growth of express delivery volume in China and the increasing customer demands for express service level, the shortcomings of the traditional air express transportation with higher costs and more transportation restrictions, and road express transportation with high energy consumption and emissions are becoming increasingly obvious, which restricts the further development of the express delivery industry. By using high-speed railway to undertake the trunk transportation tasks of express goods and optimizing the terminal pick-up and delivery network that connects high-speed railway with urban areas, transportation efficiency and customer satisfaction can be improved. Based on the design of high-speed railway express terminal pick-up and delivery network, this paper established a double-layer multiobjective optimization model with the upper layer of transshipment and lower layer of pick-up and distribution with the optimization objectives of minimizing transportation cost and maximizing customer satisfaction. Considering the multi-objective characteristics of the model, a non-dominated sorting genetic algorithm, NSGA-II, was designed and improved for solving bi-level optimization problems. The proposed method was verified, and the solution results show that the algorithm converges at 55. 9 generations on average, and the final pareto optimal solution set includes 10 solutions with crowding distance around 0. 5. Among these solutions, the highest satisfaction rate of meeting the requirement for high-speed railway next-day delivery is 97%, with the lowest of 92%, and the average of 94. 3%. © 2024 Science Press. All rights reserved.
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页码：11 / 21
页数：10
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