Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China

Abstract

In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.

A Systematic Literature Review: Optimization Timetable in Education to Support Work-Life Balance (WLB)

“…Zandavi et al (2021) proposed a hybrid optimization technique that combines the Nelder-Mead Simplex algorithm with the Nondominated Sorting Genetic Algorithm to optimise the scheduling problem for distant laboratories to coordinate. Wang et al (2019) developed a particle swarm optimization technique based on a genetic algorithm, in which the position vector and genetic evolution operators are reconstructed based on each train's departure and arrival timings at stations. The latest studied by Guerriero and Guido (2022) developed a novel optimization to improve employees' satisfaction and ensure the best work-life balance possible, an alternative partition of a workday into shifts to the usual two shifts, morning, and afternoon.…”

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

“…(16) D i s (2) i = s (1) i+1 , s (2) i+1 , s (3) i+1 (17) D i s (3) i = s (3) i+1 , s (4) i+1 (18) i+1 = D i s (1) i ∪ D i s (1) i ∪ D i s (2) i = s (1) i+1 , s (2) i+1 , s (3) i+1 , s (4) i+1 (19)…”

“…S. Dündar et al [17] used artificial neural networks and genetic algorithms to solve train rescheduling for single-track railways. M. Wang et al [18] developed a genetic algorithm based on the particle swarm optimization (PSO) to solve the TRP under primary delays. Y. Zhang et al [19] studied the problem of re-optimization of train platform in case of train delays, where the station is modeled using the discretization of the platform track time-space resources.…”

Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

“…Learning factors c 1 , c 2 also affect the searchability of the algorithm. Smaller learning factors will let the particles wander in the range away from the target area, while larger learning factors will fly particles as soon as possible to the target area to converge [39]. In this paper, the non-linear symmetry method is used to update the learning factors c 1 , c 2 .…”