Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays
Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays
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DOI：
10.1007/s00500-015-1924-x
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发表时间：
2016-08-01
期刊：
影响因子：
4.1
通讯作者：
Mavrovouniotis, Michalis
中科院分区：
文献类型：
--
作者：
Eaton, Jayne;Yang, Shengxiang;Mavrovouniotis, Michalis
列车运行中断后的重新调度是一项具有挑战性的任务，也是铁路行业的一个重要问题，因为列车晚点可能导致巨额罚款、客户不满和收入损失。有时候，在很短的时间内可能会发生不止一个延迟，而是几个不相关的延迟，这使得问题更具挑战性。此外，这个问题是一个动态的问题，随着时间的推移而变化，因为列车在交汇处等待重新安排，更多的列车将到达，这将改变问题的性质。本研究的目的是探讨几种不同的蚁群优化（ACO）算法的应用程序的问题的动态列车延误的情况下，多个延误。该算法不仅重新排序的列车在枢纽，但也重新排序的列车在车站，这被认为是第一步，扩大问题，考虑更大的区域的铁路网络。结果表明，在动态重调度问题中，带记忆的蚁群算法比仅利用信息素蒸发去除冗余信息素踪迹的蚁群算法能更好地应对动态变化.此外，已经表明，如果记忆中的蚂蚁解决方案变得不可挽回地不可行，则可以基于迄今为止最好的蚂蚁，用精英移民取代它们，并且仍然获得良好的性能。
Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance.