Collaborative optimization of high-speed railway track group maintenance and train scheduling

The high operational speed and density of high-speed rail systems intensify the friction between rolling stock and track infrastructure, resulting in accelerated wear of track components and an increased risk of failure. Consequently, periodic track maintenance is essential to ensure operational safety and infrastructure integrity. However, even when maintenance is scheduled during nighttime windows, it can disrupt regular train services. Moreover, such operations typically require the deployment of large-scale, high-cost maintenance machinery, the availability of which is often limited. To address these operational and logistical challenges, this study proposes a collaborative optimization approach for integrated group maintenance planning and train scheduling, incorporating structural dependencies within the track system. The optimization model aims to minimize total maintenance costs, train delays, and track occupancy duration. The problem is formulated and solved using a genetic algorithm implemented in MATLAB, generating tailored maintenance schedules and train dispatching schemes for various maintenance demand scenarios. Simulation results indicate that the proposed strategy effectively reduces both maintenance duration and associated costs. Future research will focus on extending the applicability of the model from single-line railways to broader, more complex railway networks. This will involve augmenting the mixed-integer linear programming framework with additional constraints and variables to more accurately represent real-world operating conditions, such as variable train velocities and differences in braking performance across train models.