Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach

Lingyun Meng, Xuesong Zhou

Research output: Contribution to journalArticle

105 Citations (Scopus)

Abstract

After a major service disruption on a single-track rail line, dispatchers need to generate a series of train meet-pass plans at different decision times of the rescheduling stage. The task is to recover the impacted train schedule from the current and future disturbances and minimize the expected additional delay under different forecasted operational conditions. Based on a stochastic programming with recourse framework, this paper incorporates different probabilistic scenarios in the rolling horizon decision process to recognize (1) the input data uncertainty associated with predicted segment running times and segment recovery times and (2) the possibilities of rescheduling decisions after receiving status updates. The proposed model periodically optimizes schedules for a relatively long rolling horizon, while selecting and disseminating a robust meet-pass plan for every roll period. A multi-layer branching solution procedure is developed to systematically generate and select meet-pass plans under different stochastic scenarios. Illustrative examples and numerical experiments are used to demonstrate the importance of robust disruption handling under a dynamic and stochastic environment. In terms of expected total train delay time, our experimental results show that the robust solutions are better than the expected value-based solutions by a range of 10-30%.

Original languageEnglish (US)
Pages (from-to)1080-1102
Number of pages23
JournalTransportation Research Part B: Methodological
Volume45
Issue number7
DOIs
StatePublished - Aug 2011
Externally publishedYes

Fingerprint

scenario
Stochastic programming
Railroad tracks
Rails
Time delay
recourse
Recovery
programming
uncertainty
time
Rolling horizon
Dispatching
Scenarios
Train
experiment
Experiments
Schedule
Disruption
Rescheduling
Uncertainty

Keywords

  • Disruption handling
  • Rolling horizon decision making
  • Stochastic optimization
  • Train dispatching

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Transportation

Cite this

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abstract = "After a major service disruption on a single-track rail line, dispatchers need to generate a series of train meet-pass plans at different decision times of the rescheduling stage. The task is to recover the impacted train schedule from the current and future disturbances and minimize the expected additional delay under different forecasted operational conditions. Based on a stochastic programming with recourse framework, this paper incorporates different probabilistic scenarios in the rolling horizon decision process to recognize (1) the input data uncertainty associated with predicted segment running times and segment recovery times and (2) the possibilities of rescheduling decisions after receiving status updates. The proposed model periodically optimizes schedules for a relatively long rolling horizon, while selecting and disseminating a robust meet-pass plan for every roll period. A multi-layer branching solution procedure is developed to systematically generate and select meet-pass plans under different stochastic scenarios. Illustrative examples and numerical experiments are used to demonstrate the importance of robust disruption handling under a dynamic and stochastic environment. In terms of expected total train delay time, our experimental results show that the robust solutions are better than the expected value-based solutions by a range of 10-30{\%}.",
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