Railway platform reallocation after dynamic perturbations using ant colony optimisation
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.
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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
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes