Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30845
Title: Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays
Authors: Eaton, Jayne 
Yang, Shengxiang 
Mavrovouniotis, Michalis 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Ant colony optimization;Dynamic optimization problem;Dynamic railway junction rescheduling;Rail transportation;UK railway network
Issue Date: 1-Aug-2016
Source: Soft Computing, 2016, vol. 20, iss. 8, pp. 2951 - 2966
Volume: 20
Issue: 8
Start page: 2951
End page: 2966
Journal: Soft Computing 
Abstract: 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.
URI: https://hdl.handle.net/20.500.14279/30845
ISSN: 14327643
DOI: 10.1007/s00500-015-1924-x
Rights: © The Author(s)
Type: Article
Affiliation : De Montfort University 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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