A Multistage Decision Optimization Approach for Train Timetable Rescheduling Under Uncertain Disruptions in a High-Speed Railway Network

Railway operations are vulnerable to unexpected disruptions that should be handled in an efficient and passenger-friendly way. This paper focuses on a real-time train timetable rescheduling problem in a railway system with a seat-reserved mechanism during disruptions. An integer linear programming model with a compensation mechanism is established to provide a better overall solution for dynamic and multistage rescheduling problems. The main part of the model considers the strategies of retiming, adding train stops and reordering to reduce the impact of passengers, while the compensation mechanism part mainly deals with the uncertainty of disruption durations. The related constraints mainly include section track capacity, station track capacity,passenger transfer rules and some robust measures under the uncertainty of disruption durations. A multistage decision optimization framework is established, and a rolling horizon algorithm is embedded in it to approximate the desired optimal solution. A series of numerical experiments based on a real case of China's high-speed railway subnetwork are carried out to verify the effectiveness and efficiency of the proposed optimization approach.