A Memetic Algorithm for High-Speed Railway Train Timetable Rescheduling

Abstract

This study addresses a high-speed railway train timetable rescheduling (TTR) problem with a complete blockage at the station and train operation constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that minimizes the weighted sum of the total delay time of trains. A memetic algorithm (MA) is proposed, and the individual of MA is represented as a permutation of trains’ departure order at the disrupted station. The individual is decoded to a feasible schedule of the trains using a rule-based method to allocate the running time in sections and dwell time at stations. Consequently, the original problem is reformulated as an unconstrained problem. Several permutation-based operators are involved, including crossover, mutation, and local search. A restart strategy was employed to maintain the the population diversity. The proposed MA was compared with the first-scheduled-first-served (FSFS) algorithm and other state-of-the-art evolutionary algorithms. The experimental results demonstrate the superiority of MA in solving the TTR through permutation-based optimization in terms of constraint handling, solution quality, and computation time.

Bus Rescheduling for Long-Term Benefits: An Integrated Model Focusing on Service Capability and Regularity

“…Moreover, Long [25] took the maximum train-loading rate into account. Researchers [26][27][28][29] have also added deviations from departure intervals to models to satisfy service requirements. Service regularity has gradually become increasingly crucial during rescheduling, which can be represented by departure intervals.…”

Key technologies and applications of intelligent dispatching command for high-speed railway in China

“…For this problem, the common solving methods mainly refers to operation research, intelligent optimization (evolutionary computation) and AI algorithm (e.g. reinforcement learning) (Ding, Zhang, Liu et al. , 2022; Ding, Zhang, Wang, & Yuan, 2022; Wang, Zhang, Zhang, Wang, & Ding, 2022; Yan, Zhang, Ding, & Wang, 2022).…”

“…For this problem, the common solving methods mainly refers to operation research, intelligent optimization (evolutionary computation) and AI algorithm (e.g. reinforcement learning) (Ding, Zhang, Liu et al, 2022;Yan, Zhang, Ding, & Wang, 2022). In order to realize conflict detection and resolution in the dynamic environment, the model and framework of automatic adjustment are constructed.…”