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    Ant colony optimization for simulated dynamic multi-objective railway junction rescheduling

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    Main article (3.120Mb)
    Date
    2017-03-10
    Author
    Eaton, Jayne;
    Yang, Shengxiang;
    Gongora, Mario Augusto
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    Abstract
    Minimising 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.
    Description
    open access article
    Citation : Eaton, 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. 2980-2992
    URI
    http://hdl.handle.net/2086/13211
    DOI
    https://dx.doi.org/10.1109/TITS.2017.2665042
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2968]

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