Coordinated Optimization Research of High-Speed Train Timetable and Maintenance Window

To resolve the inherent conflict between train timetable and maintenance window in high-speed railways, this study proposes a coordinated optimization approach integrating both aspects. In emergency scenarios such as train delays or extended maintenance durations, railway operators must rapidly formulate delay management strategies to mitigate disruptions. To address this challenge, a multi-objective mixed-integer programming model is proposed, which simultaneously minimizes train delay time and maintenance window extension time while incorporating train timetable rescheduling and maintenance window constraints. The model is solved using a multi-objective optimization algorithm incorporating stratified sequencing, with computational implementation in Python leveraging the Gurobi solver. The proposed methodology is validated through an experiment study on the Beijing-Shanghai High-Speed Railway. The computational outcomes confirm that the integrated optimization model successfully minimizes cumulative delays across train operations and maintenance activities, while satisfying all rescheduling restrictions and technical maintenance specifications. These findings provide railway operation management departments with data-driven decision-making tools to enhance train timetable robustness and maintenance efficiency in emergency scenarios.