A Systematic Approach to the High-Level Maintenance Scheduling for High-Speed Trains in China

High-level maintenance is one of the key practices to ensure the safe operation of high-speed trains (HSTs). However, it is usually scheduled manually, and there has been a lack of systematic study on how to optimize maintenance scheduling considering a dynamic time interval. This article discusses the state-of-the-art high-level maintenance scheduling models of HSTs. The one-time maintenance scheduling problem, where the maintenance action occurs at most once in the planning horizon, is a key issue for HSTs' high-level maintenance. We construct a more efficient mathematical model and propose an exact solution for the one-time scheduling model (OSM). However, HSTs might be maintained several times within the planning horizon to achieve a more favorable performance in real-world practice. Therefore, we extend the OSM to the periodic scheduling model (PSM), which aims to accommodate several maintenance actions and could be valuable to obtain a global optimal plan. Both models are mixed 0-1 linear integer models and can be solved by commercial solvers. The effectiveness and comparisons of the proposed models are illustrated by a real-case study using the maintenance records of 100 HSTs in China. The results can serve as the basis for the HSTs' high-level maintenance scheduling under either a periodic case or a one-time case.

Automated summary

High-level maintenance is crucial for safe operation of high-speed trains, but manual scheduling lacks optimization based on dynamic time intervals. This article presents state-of-the-art scheduling models for high-level maintenance of high-speed trains. The one-time maintenance scheduling problem is a key issue, and a more efficient mathematical model and solution are proposed. Additionally, a periodic scheduling model is developed to accommodate multiple maintenance actions and achieve global optimal plans. Both models are mixed 0-1 linear integer models solvable by commercial solvers. Real-case study using 100 high-speed trains in China validates the effectiveness and comparisons of the proposed models, serving as a basis for high-level maintenance scheduling in either periodic or one-time cases.