Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways

Dündar, Selim; Şahin, İsmail

doi:10.1016/j.trc.2012.11.001

Cited by 116 publications

(48 citation statements)

References 18 publications

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“…Therefore, Krasemann [9] designed a greedy algorithm to quickly find a good solution by performing a depth-first search. Dündar and S, ahin [10] developed a genetic algorithm to minimize the total weighted delay. The algorithm could reduce total delay time by around half in comparison to an artificial neural network method developed to mimic the decision behavior of dispatchers.…”

Section: Timetablementioning

confidence: 99%

“…Examples include greedy algorithm [9], particle swarm algorithm [25], and genetic algorithm [10]. However, in this study, we utilize the -constraint method [26] to convert the model into a Journal of Advanced Transportation 5 single-objective model, and some linearization techniques are adopted to reformulate all nonlinear constraints into linear constraints.…”

Section: Model Solutionmentioning

confidence: 99%

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A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

Zhao

Ning

2018

Journal of Advanced Transportation

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This study proposes a biobjective optimization method for timetable rescheduling during the end-of-service period of a subway network, taking all stakeholders' interests into consideration. We seek to minimize the total transfer waiting time for all transfer passengers, meanwhile minimizing the deviation to the scheduled timetable. The -constraint method and linearization techniques are utilized to obtain the approximate Pareto optimal solutions within limited seconds, allowing for figuring out the trade-off between the two objectives. The method is validated by numerical experiments for different delay scenarios based on a real-world case: the Beijing subway network.

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Section: Timetablementioning

confidence: 99%

Section: Model Solutionmentioning

confidence: 99%

A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

Zhao

Ning

2018

Journal of Advanced Transportation

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“…In each step, an advanced branch and bound algorithm was used to solve the sub-problems optimality. D undar and S ahin [27] designed a decision support system using Genetic Algorithms (GAs) and Arti cial Neural Networks (ANNs) for real-time con ict resolution problem. The methodology was tested with actual data extracted from train operations in Turkish State Railways.…”

Section: Literature Reviewmentioning

confidence: 99%

A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions

2017

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Abstract. Delays and disruptions reduce the reliability and stability of the rail operations.Railway tra c rescheduling includes ways to manage the operations during and after the occurrence of such disturbances. In this study, we consider the simultaneous presence of large disruptions (temporary full or partial blockage of tracks) as well as stochastic variation of operations as a source of disturbance. The occurrence time of blockage and its recovery time are given. We designed a simulation-based optimization model that incorporates dynamic dispatch priority rules with the objective of minimizing the total delay time of trains. We, moreover, designed a variable neighborhood search meta-heuristic scheme for handling tra c under the limited capacity close to the blockage. The new plan includes a set of new departure times, dwell times, and train running times. We evaluated the proposed model on a set of disruption scenarios covering a large part of the Iranian rail network. The result indicates that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly, when compared to commercial optimization software. In addition, the solutions have a lower average and smaller standard deviation than the currently accepted solutions, determined by human dispatcher or by standard software packages.

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“…The energy consumption index J for a high-speed train can be calculated as bas aux J J J  (10) where bas J is the basic traction energy derived by the traction actions, which correspond to the traction and cruising conditions, and aux J represents the auxiliary energy component. These two components can be computed as…”

Section: Traction Model and Energy Consumption Analysismentioning

confidence: 99%

Energy Consumption Modeling and Optimization of Traction Control for High-speed Railway Trains

Liu

Cai²,

Xu³

et al. 2015

IJCA

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Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways

Cited by 116 publications

References 18 publications

A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions

Energy Consumption Modeling and Optimization of Traction Control for High-speed Railway Trains

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