Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/6090742 |
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| _version_ | 1832548572138045440 |
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| author | Mingming Wang Li Wang Xinyue Xu Yong Qin Lingqiao Qin |
| author_facet | Mingming Wang Li Wang Xinyue Xu Yong Qin Lingqiao Qin |
| author_sort | Mingming Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d5603e326fb845f7b1847dcebbe954b4 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d5603e326fb845f7b1847dcebbe954b42025-02-03T06:13:47ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/60907426090742Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in ChinaMingming Wang0Li Wang1Xinyue Xu2Yong Qin3Lingqiao Qin4School of Traffic and Transportation, State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaTOPS Laboratory, Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Wisconsin, USAIn 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.http://dx.doi.org/10.1155/2019/6090742 |
| spellingShingle | Mingming Wang Li Wang Xinyue Xu Yong Qin Lingqiao Qin Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China Journal of Advanced Transportation |
| title | Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China |
| title_full | Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China |
| title_fullStr | Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China |
| title_full_unstemmed | Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China |
| title_short | Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China |
| title_sort | genetic algorithm based particle swarm optimization approach to reschedule high speed railway timetables a case study in china |
| url | http://dx.doi.org/10.1155/2019/6090742 |
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