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.
2016 IEEE Symposium Series on Computational Intelligence (SSCI)