A resource-oriented decomposition approach for train timetabling problem with variant running time and minimum headway. Issue 2 (7th February 2022)

• Record Type: Journal Article
• Authors: Liao, Zhengwen; Miao, Jianrui; Meng, Lingyun; Li, Haiying
• Is Part Of: Transportation letters. Volume 14:Issue 2(2022)
• Page Start: 129
• Page End: 142
• Publication Date: 2022-02-07
• Subjects: train timetabling -- scheduling -- optimization -- cumulative flow variable -- Lagrangian relaxation -- parallel computation; Transportation -- Research -- Periodicals; Transportation -- Periodicals; Urban transportation -- Periodicals; 388.072
• DOI: 10.1080/19427867.2020.1824310
• Languages: English
• ISSNs: 1942-7867

Abstract

ABSTRACT: Solving the practical train timetabling problem under complex real-life train operation environment is challenging. This article addresses the train timetabling problem considering the variant parameters (i.e. running time and minimum headway) depending on stop-decisions. Based on a resource-oriented decomposition representation of safety headway, the train timetabling is modeled by cumulative flow variables considering the variant parameters depending on stop-decisions. A Lagrangian relaxation-based approach (LR) is used to decompose the combinatorial train timetabling problem into train-independent shortest path sub-problems, which can be solved simultaneously by parallel computation by relaxing the capacity constraint. A capacity assessment-based heuristic is proposed for improving the feasibility reparing of LR solutions. The solution quality and efficiency are analyzed employing the real-life operational data of Wuhan to Guangzhou high-speed railway in China. The benefits of the improved heuristic and parallel computation are demonstrated in contrast with the existed approach.