Robust Capacitated Train Rescheduling with Passenger Reassignment under Stochastic Disruptions

During railway operations unexpected events may occur, influencing normal traffic flows. This paper focuses on a train rescheduling problem in a railway system with seat-reserved mechanism during large disruptions, such as a rolling stock breakdown leading to some canceled services, where passenger reassignment strategies have also to be considered. A novel mixed-integer linear programming formulation is established with consideration of train retiming, reordering, and reservicing. Based on a time–space modeling framework, a big-M approach is adopted to formulate the track occupancy and extra train stops. The formulation aims to maximize the passenger accessibility measured by the amount of the transported passengers subject to canceled services and to minimize the weighted total train delay for all trains at their destinations. The proposed mathematical formulation also considers planning extra stops for non-canceled trains to transport the disrupted passengers, which were supposed to travel on the canceled services, to their pre-planned destinations. Other constraints deal with seat capacity limitation, track capacity, and some robustness measures under uncertainty of disruption durations. We propose different approaches to compute advanced train dispatching decisions under a dynamic and stochastic optimization environment. A series of numerical experiments based on a part of “Beijing–Shanghai” high-speed railway line is carried out to verify the effectiveness and efficiency of the proposed model and methods