Modelling disruptions and resolving conflicts optimally in a railway schedule

Narayanaswami, Sundaravalli; Rangaraj, Narayan

doi:10.1016/j.cie.2012.08.004

Cited by 71 publications

(22 citation statements)

References 22 publications

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“…Some recent studies on this area include Carey and Lockwood [20], Higgins et al [21], Caprara et al [13], Zhou and Zhong [22], Caprara et al [23], Carey and Crawford [24], Zhou and Zhong [25], D'Ariano et al [26], Burdett and Kozan [27], Liu and Kozan [28], Burdett and Kozan [8], Cacchiani et al [29], Castillo et al [15], Liu and Kozan [6], Harrod [30], Narayanaswami and Rangaraj [31], etc. The non-periodical timetables are more practical for real-time scheduling or rescheduling.…”

Section: Literature Reviewmentioning

confidence: 96%

A simulated annealing algorithm for first train transfer problem in urban railway networks

Kang

Zhu

2016

Applied Mathematical Modelling

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a b s t r a c tPassengers often have to transfer between different subway lines to reach their destinations. Time coordination of first trains between feeder and connecting lines plays an important role in reducing passenger transfer waiting time. This paper addresses the first train synchronization problem, and proposes a first train coordination model which aims at minimizing total passenger transfer waiting time. Taking into account the specification of the first train problem, we use mixed-integer variables to enable the correct calculation of the waiting time for the ''first available'' train at each transfer station. In addition, we develop a simulated annealing algorithm to deal with a case study of the Beijing subway network. Results indicate that the proposed approach reduces the passenger waiting time from 705.1 min of the original first train timetable to 567.42 min of the scheduled one.

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Section: Literature Reviewmentioning

confidence: 96%

A simulated annealing algorithm for first train transfer problem in urban railway networks

Kang

Zhu

2016

Applied Mathematical Modelling

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“…In this case, a new timetable should be found while all the problem constraints are respected. Generally, the objective function consists on the minimization of train delays [11]- [12]- [13]. Dorfman and Medanic propose a discrete event model to solve their strategy (called feedbackbased travel advance strategy).…”

Section: Nd Class: Dynamic or Reactive Problemmentioning

confidence: 99%

Optimization of a Train Traffic Management Problem under Uncertainties and Disruptions

Mesghouni¹

2014

SIC

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In this paper, we consider a train traffic management problem. Our aim is to find an optimal schedule for a train traffic network where time duration uncertainties are considered. This problem was intensively studied with mixed integer linear models where trains moving duration are deterministic. In this paper, a new formulation of the problem as a classical one with scenario-based stochastic programming taking expected values as objective functions is presented. Then, new criterion is proposed to quantify scheduling robustness in the face of uncertainty. Besides, a novel control policy is elaborated to find quickly, a feasible train schedule when disruption or unexpected event occurs during scheduling execution.

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“…For both approaches, the authors face increasing difficulty to obtain a feasible timetable for larger time horizons. Narayanaswami and Rangaraj (2013) develop a MILP model that resolves conflicts caused by a disruption that blocks part of a single bidirectional line. Trains can only meet and pass each other in the stations, and cannot be cancelled.…”

Section: Literature Reviewmentioning

confidence: 99%