Dynamic railway junction rescheduling using population based ant colony optimisation
Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Eaton, J. and Yang, S. (2014) Dynamic railway junction rescheduling using population based ant colony optimisation. Proceedings of the 2014 UK Workshops on Computational Intelligence (UKCI), Bradford, UK, pp. 1-8.
ISBN : 9781479955381
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes