Train timetabling for an urban rail transit line using a Lagrangian relaxation approach

Hassannayebi, Erfan; Zegordi, Seyed Hessameddin; Yaghini, Masoud

doi:10.1016/j.apm.2016.06.040

Cited by 68 publications

(26 citation statements)

References 39 publications

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“…The design of a demand-oriented train timetable is a complex process that involves many resources. 24 An overview of contributions in this issue is highlighted as follows: In an early work by Cury et al, 25 a train operation optimization model was developed for underground rail transit system taking into account the train and passengers actions, service levels for passengers, and fleet size. Bandara and Ekanayake 26 designed a train scheduling model that minimizes operational delays subject to service constraints on single-track rail lines.…”

Section: Introductionmentioning

confidence: 99%

Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

Shakibayifar

Hassannayebi

Jafary

et al. 2017

Appl Stoch Models Bus & Ind

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Urban rail planning is extremely complex, mainly because it is a decision problem under different uncertainties. In practice, travel demand is generally uncertain, and therefore, the timetabling decisions must be based on accurate estimation. This research addresses the optimization of train timetable at public transit terminals of an urban rail in a stochastic setting. To cope with stochastic fluctuation of arrival rates, a two‐stage stochastic programming model is developed. The objective is to construct a daily train schedule that minimizes the expected waiting time of passengers. Due to the high computational cost of evaluating the expected value objective, the sample average approximation method is applied. The method provided statistical estimations of the optimality gap as well as lower and upper bounds and the associated confidence intervals. Numerical experiments are performed to evaluate the performance of the proposed model and the solution method.

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Section: Introductionmentioning

confidence: 99%

Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

Shakibayifar

Hassannayebi

Jafary

et al. 2017

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

show abstract

“…The management of train timetable is a complex procedure subject to the capacity and resource constraints [16]. This problem belongs to a widerange class of combinatorial optimization models and methods being called railway disruption management.…”

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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“…Train timetabling problem basically consists of determining the train's departure and arrival times in each station. The assessment of train timetable is a complex procedure due to the fact that it is subjected to the capacity and resource constraints [3]. A majority of the approaches to train timetabling are analytical methods.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated train scheduling and infrastructure development model in railway networks

et al. 2017

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Abstract. The evaluation of the railway infrastructure capacity is an important task of railway companies. The goal is to nd the best infrastructure development plan for scheduling new train services. The question addressed by the present study is how the existing railway infrastructure can be upgraded to decrease the total delay of existing and new trains with minimum cost. To answer this question, a mixed-integer programming formulation is extended for the integrated train scheduling and infrastructure development problem. The train timetabling model deals with the optimum schedule of trains on a railway network and determines the best stop locations for both the technical and religious services. We developed two heuristics based on variable xing strategies to reduce the complexity of the problem. To evaluate the e ect of railway infrastructure development on the schedule of new trains, a sequential decomposition is adopted for Iranian railway network. The outcomes of the empirical analysis performed in this study allow to gain bene cial insights by identifying the bottleneck corridors. The result of the proposed methodology shows that it can signi cantly decrease the total delay of new trains with the most emphasis on the bottleneck sections.

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Train timetabling for an urban rail transit line using a Lagrangian relaxation approach

Cited by 68 publications

References 39 publications

Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

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

An integrated train scheduling and infrastructure development model in railway networks

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