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%.
Keywords
Train dispatching,    Disruption handling,    Rolling horizon decision
making ,    Stochastic optimization
Highlights
► Recover impacted single-track train schedule from current and future disturbances.
► Find robust solutions based on a two-stage stochastic programming framework.
► Systematically construct and select meet-pass plans under different scenarios.
► Robust solutions are better than expected value-based solutions by 10–30%.
原文传递: https:/www.sciencedirect.com/science/article/pii/S0191261511000518