Railway platform reallocation after dynamic perturbations using ant colony optimisation

Jayne Eaton, Shengxiang Yang
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引用次数: 2

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.
基于蚁群优化的动态扰动后铁路站台再分配
在复杂繁忙的铁路网中,火车在车站延误是经常发生的事情。延误的列车将错过其在站台上的预定时段,可能不得不重新分配到一个新的站台,以允许它继续其旅程。这个问题是动态的,因为在重新分配延误的列车时,可能会发生更多意想不到的列车延误,随着时间的推移,改变问题的性质。本研究的目的是将蚁群优化(ACO)应用于一个动态平台再分配问题(DPRP),使用一个从真实世界的列车时刻表数据创建的模型。为了确保列车不会不必要地重新分配到新的月台,我们引入了一种新颖的最佳反替代方案,该方案不仅考虑了客观价值,还考虑了原月台与新月台之间的物理距离。结果表明,蚁群算法优于启发式算法,启发式算法将延迟列车放置在第一个可用时隙中,并且这种改进在高频动态变化中更为明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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