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    Railway platform reallocation after dynamic perturbations using ant colony optimisation

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    Main article (319.4Kb)
    Date
    2016-12
    Author
    Eaton, Jayne;
    Yang, Shengxiang
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    Abstract
    Train delays at stations are a common occurrence in complex, busy railway networks. A delayed train will miss its scheduled time slot on the platform and may have to be reallocated to a new platform to allow it to continue its journey. The problem is a dynamic one because while reallocating a delayed train further unanticipated train delays may occur, changing the nature of the problem over time. Our aim in this study is to apply ant colony optimisation (ACO) to a dynamic platform reallocation problem (DPRP) using a model created from real-world train schedule data. To ensure that trains are not unnecessarily reallocated to new platforms we introduce a novel best-ant-replacement scheme that takes into account not only the objective value but also the physical distance between the original and the new platforms. Results showed that the ACO algorithm outperformed a heuristic that places the delayed train in the first available time-slot and that this improvement was more apparent with high-frequency dynamic changes.
    Description
    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. (2016) Railway platform reallocation after dynamic perturbations using ant colony optimisation. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016
    URI
    http://hdl.handle.net/2086/12781
    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 [2682]

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