Multi-stage timetable rescheduling for high-speed railways: a dynamic programming approach with adaptive state generation

Abstract A dynamic programming (DP) approach with adaptive state generation and conflicts resolution is developed to address the timetable-rescheduling problem (TRP) at relatively lower computation costs. multi-stage decision-making model first represent procedure in high-speed railways. Then, an method by reordering trains each station proposed dynamically create possible states according of previous stages, such that infeasible can be removed search space reduced. are resolved retiming arrival and/or departure times trains. Furthermore, transfer equation built Bellman derive solution minimize total delay time (TT). series simulation experiments a real-world case study used evaluate performance method. The indicate able find optimal timetable appropriate overtaking right stations reduce 62.7% 41.5% respect First-Come-First-Serve (FCFS) First-Schedule-First-Serve (FSFS) strategy widely practice. Comparing intelligent scheduling (e.g., Ant Colony Optimization Particle Swarm Optimization), similar objective achieved much cost time, which make more applicable TRP daily operation railway.