火车
地铁列车时刻表
计算机科学
随机规划
持续时间(音乐)
运筹学
数学优化
北京
集合(抽象数据类型)
分解
水准点(测量)
工程类
数学
中国
法学
艺术
生态学
地图学
文学类
大地测量学
政治学
生物
程序设计语言
地理
操作系统
作者
Hangyu Ji,Rui Wang,Chuntian Zhang,Jiateng Yin,Lin Ma,Lixing Yang
出处
期刊:Omega
[Elsevier]
日期:2023-11-13
卷期号:124: 102999-102999
被引量:3
标识
DOI:10.1016/j.omega.2023.102999
摘要
In high-speed railways, unexpected disturbances on maintenance activities may cause serious delays of the scheduled trains and greatly affect the service quality for traveling passengers. In contrast to most existing studies that focused on deterministic maintenance activities, this paper develops a two-stage stochastic programming approach to address the optimization of train schedules under uncertain maintenance plans. Specifically, in the first stage, we aim to determine the departure times of trains from the origin station, since this information needs to be public to passengers in advanced. The objective function is to minimize the expected travel time of trains under uncertain duration time of maintenance activities. In the second stage, given the specific information of maintenance activities, we generate the train schedule by adjusting the stop patterns, train orders and the assignment of tracks at key stations. Due to the computational difficulties arising from the large number of discrete decision variables, we particularly develop a dual decomposition based solution approach to solve the two-stage stochastic model. Our approach decomposes the original problem into a set of scenario-dependent subproblems with much fewer number of variables, which greatly improves the computational efficiency. Finally, we conduct several sets of real-world instances based on the Beijing–Guangzhou high-speed railway corridor to verify the effectiveness of the proposed model and solution approach. The results demonstrate that our approach evidently outperforms state-of-art solvers (Gurobi), especially for large-scale instances that Gurobi cannot even return feasible solutions.
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