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

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