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dc.contributor.authorEaton, Jayneen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorGongora, Mario Augustoen
dc.date.accessioned2017-02-06T15:30:10Z
dc.date.available2017-02-06T15:30:10Z
dc.date.issued2017-03-10
dc.identifier.citationEaton, J., Yang, S. and Gongora, M. A. (2017) Ant colony optimization for simulated dynamic multi-objective railway junction rescheduling. IEEE Transactions on Intelligent Transportation Systems, 18 (11), pp. 2980-2992en
dc.identifier.urihttp://hdl.handle.net/2086/13211
dc.descriptionopen access article
dc.description.abstractMinimising the ongoing impact of train delays has benefits to both the users of the railway system and the railway stakeholders. However, the efficient rescheduling of trains after a perturbation is a complex real-world problem. The complexity is compounded by the fact that the problem may be both dynamic and multi-objective. The aim of this research is to investigate the ability of ant colony optimisation algorithms to solve a simulated dynamic multi-objective railway rescheduling problem and, in the process, to attempt to identify the features of the algorithms that enable them to cope with a multi-objective problem that is also dynamic. Results showed that, when the changes in the problem are large and frequent, retaining the archive of non-dominated solution between changes and updating the pheromones to reflect the new environment play an important role in enabling the algorithms to perform well on this dynamic multi-objective railway rescheduling problem.en
dc.language.isoen_USen
dc.publisherIEEEen
dc.subjectTrain reschedulingen
dc.subjectdynamic multi-objective optimizationen
dc.subjectant colony optimisationen
dc.subjectrail transportationen
dc.subjectUK railway networken
dc.titleAnt colony optimization for simulated dynamic multi-objective railway junction reschedulingen
dc.typeArticleen
dc.identifier.doihttps://dx.doi.org/10.1109/TITS.2017.2665042
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-02-02en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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