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dc.contributor.authorEaton, Jayne
dc.contributor.authorYang, Shengxiang
dc.date.accessioned2020-01-07T09:20:10Z
dc.date.available2020-01-07T09:20:10Z
dc.date.issued2014-10-20
dc.identifier.citationEaton, 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.en
dc.identifier.isbn9781479955381
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/18983
dc.descriptionThe 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.en
dc.description.abstractEfficient 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.en
dc.language.isoenen
dc.publisherIEEE Pressen
dc.subjectTrain rescheduling problemen
dc.subjectant colony optimisationen
dc.subjectdynamic railway junction rescheduling problemen
dc.titleDynamic railway junction rescheduling using population based ant colony optimisationen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/ukci.2014.6930174
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.cclicenceN/Aen
dc.date.acceptance2014-06
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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