Collaborative Optimization for Overnight Train Operation and Maintenance Window Setting of High-Speed Railways

XU Chang’an; LI Shengdong; LI Sihan; NI Shaoquan

doi:10.3969/j.issn.0258-2724.20191205

Volume 56 Issue 4

Jul. 2021

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Journal of Southwest Jiaotong University > 2021 > 56(4): 744-751.

XU Chang’an, LI Shengdong, LI Sihan, NI Shaoquan. Collaborative Optimization for Overnight Train Operation and Maintenance Window Setting of High-Speed Railways[J]. Journal of Southwest Jiaotong University, 2021, 56(4): 744-751. doi: 10.3969/j.issn.0258-2724.20191205

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XU Chang’an, LI Shengdong, LI Sihan, NI Shaoquan. Collaborative Optimization for Overnight Train Operation and Maintenance Window Setting of High-Speed Railways[J]. Journal of Southwest Jiaotong University, 2021, 56(4): 744-751. doi: 10.3969/j.issn.0258-2724.20191205

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Collaborative Optimization for Overnight Train Operation and Maintenance Window Setting of High-Speed Railways

doi: 10.3969/j.issn.0258-2724.20191205

Received Date: 19 Jan 2020
Rev Recd Date: 19 May 2020

Available Online: 29 Sep 2020

Publish Date: 15 Aug 2021

Abstract

Abstract

There is a coupling relationship between overnight train operation and maintenance window setting of high-speed railways. Their collaborative optimization contributes to meeting passenger travel demands at night and improves the allocation of railway transportation resources. Taking the corridor-type high-speed railways as the object, the analysis of the dynamic relationship between overnight train operation and maintenance window setting is performed, with the goal of minimizing the total travel time of all overnight trains and the impact of overnight train operation on the existing train timetable. A nonlinear mixed integer programming model is established to realize the collaborative optimization of overnight train operation and maintenance window in the condition of the undetermined train operation mode. Given the complexity of the problem, dimension reduction strategies such as dual-objective transformation and constraint linearization are proposed; then a heuristic algorithm based on the adaptive large neighborhood search is designed to solve the problem. Finally, a case study of Beijing−Guangzhou railway corridors was conducted to verify the method. The results show that the algorithm can converge to the optimal solution after about 40 iterations at a cost of 784 s.
- high speed railway,
- overnight trains,
- maintenance window,
- collaborative optimization,
- adaptive large neighborhood search algorithm

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References

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