Dynamic railway junction rescheduling using population based ant colony optimisation

Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency.

[1]  Shengxiang Yang,et al.  Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors , 2013, Appl. Soft Comput..

[2]  Shengxiang Yang,et al.  A memetic ant colony optimization algorithm for the dynamic travelling salesman problem , 2011, Soft Comput..

[3]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[4]  Paul Weston,et al.  A comparison of algorithms for minimising delay costs in disturbed railway traffic scenarios , 2012, J. Rail Transp. Plan. Manag..

[5]  Michael Guntsch,et al.  Applying Population Based ACO to Dynamic Optimization Problems , 2002, Ant Algorithms.

[6]  Zhou Lei-shan,et al.  A clonal selection based differential evolution algorithm for double-track railway train schedule optimization , 2010, 2010 2nd International Conference on Advanced Computer Control.

[7]  Shengxiang Yang,et al.  Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem , 2013 .

[8]  Dianye Zhang,et al.  An Intelligent Search Technique to Train Scheduling Problem Based on Genetic Algorithm , 2006, 2006 International Conference on Emerging Technologies.

[9]  Dario Pacciarelli,et al.  A branch and bound algorithm for scheduling trains in a railway network , 2007, Eur. J. Oper. Res..

[10]  Tin Kin Ho,et al.  Railway junction conflict resolution by genetic algorithm , 2000 .

[11]  Michael Francis Gorman,et al.  An application of genetic and tabu searches to the freight railroad operating plan problem , 1998, Ann. Oper. Res..

[12]  Martin Middendorf,et al.  Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP , 2001, EvoWorkshops.

[13]  Abdellah El Moudni,et al.  Perfect homogeneous rail traffic: A quick efficient genetic algorithm for high frequency train timetabling , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[14]  Tao Tang,et al.  Real-Time Train Rescheduling in Junction Areas , 2010 .

[15]  Miguel A. Salido,et al.  A Genetic Algorithm for Railway Scheduling Problems , 2008, Metaheuristics for Scheduling in Industrial and Manufacturing Applications.

[16]  Shengxiang Yang,et al.  Ant Colony Optimization with Immigrants Schemes in Dynamic Environments , 2010, PPSN.

[17]  Robert Watson,et al.  Train planning in a fragmented railway: a British perspective , 2008 .

[18]  S V Zwaan,et al.  ANT COLONY OPTIMISATION FOR JOB SHOP SCHEDULING , 1998 .