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.
Highlights
Primary delays inevitably occur during traffic operations [1] and may cause the delay propagation, especially in highspeed railway systems with dense passenger flow
(1) A mixed integer programming (MIP) model is formulated for timetable rescheduling problem, where the infrastructure capacity and rescheduling strategies such as retiming, reordering, and changing stop pattern are considered; (2) a novel genetic algorithm (GA)-particle swarm optimization (PSO) method is developed, where the position vector and genetic evolution operators are reconstructed based on the departure time and arrival time of each train at every station; (3) the proposed model and algorithm are tested on the busiest Beijing-Shanghai high-speed railway corridor with primary delays
The objective value of the proposed GA-PSO is reduced by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service (FCFS) strategy, the first-schedule-first-service (FSFS) strategy, and the PSO, respectively
Summary
Primary delays inevitably occur during traffic operations [1] and may cause the delay propagation, especially in highspeed railway systems with dense passenger flow. A GAPSO algorithm is developed in this paper to address the above-mentioned drawbacks (i.e., trapped in local optima) of the traditional PSO to solve the train timetable rescheduling problem. In this paper, accommodating retiming, reordering, and adjusting stop patterns for train services are focused on to minimize delays in a real-world corridor and the method of GA-PSO is developed. (1) A mixed integer programming (MIP) model is formulated for timetable rescheduling problem, where the infrastructure capacity and rescheduling strategies such as retiming, reordering, and changing stop pattern are considered; (2) a novel GA-PSO method is developed, where the position vector and genetic evolution operators are reconstructed based on the departure time and arrival time of each train at every station; (3) the proposed model and algorithm are tested on the busiest Beijing-Shanghai high-speed railway corridor with primary delays.
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