Allocating Railway Platforms Using A Genetic Algorithm

Clarke, Matthew J.; Hinde, Chris J.; Withall, Mark S.; Jackson, Thomas W.; Phillips, Iain; Brown, Steve; Watson, Robert Boog

doi:10.1007/978-1-84882-983-1_33

Cited by 7 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unfortunately, the work was limited by the fact that they used a very simplified railway model. Clarke et al [4] used a genetic algorithm (GA) to reallocate trains to platforms at Glasgow Central station. They found that their system could successfully allocate around 1000 trains in approximately 30 seconds, with minimal clashes between scheduled trains.…”

Section: Related Workmentioning

confidence: 99%

Railway platform reallocation after dynamic perturbations using ant colony optimisation

Eaton

Yang

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Railway platform reallocation after dynamic perturbations using ant colony optimisation

Eaton

Yang

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

View full text Add to dashboard Cite

show abstract

“…[31] In 2011 Cleark proposed genetic algorithm with six others for Railway platform allocation. [57] In 2012 Bayhan with one other proposed simulation model approach for a feasible timetable generator framework for railway scheduling problem. [30] In 2013 Corman with other three proposed iterative optimization framework for railway schedule using alternative graph method.…”

Section: Related Workmentioning

confidence: 99%

Towards Self Sustainable Railway Transportation System!

Upadhyay¹,

Joshi²

2015

CAE

View full text Add to dashboard Cite

This paper is focused on numerous challenges holding by Railway Transportation systems and various techniques to solve problems. Scheduling is one of the challenges in Railway transportation system which depends on numerous constraints. This paper consists of discussion of deadlock free schedule using Agent based system and expert system for the railway transportation system to minimize overall system delay by considering other constrain depend on this. Despite having Persist enhancement in approaches still have limitations which require improvement to meet the objective of the entire railway transportation system.

show abstract

“…Early work on our timetabling problem tackled platform allocation in a major station using real arrival times and a specified platform resource (Clarke et al, 2009). Glasgow Central has nearly 1000 trains a day and the system resolved the allocation problem in under 30 seconds on a fairly modest PC.…”

Section: Subproblemsmentioning

confidence: 99%

Train Timetable Generation Using Genetic Algorithms

Hinde¹,

Withall²,

Phillips³

et al. 2010

Proceedings of the International Conference on Evolutionary Computation

View full text Add to dashboard Cite

Abstract:The scheduling of railway trains has been a research problem for many years. Many of the choices required are not known a priori and require exploration of the problem to determine them. A modular Genetic system was designedmake the evaluation function and preparation of the timetable tractable. The Genetic system consists of a Genome, split into Chromosomes so the extra choices that become known throughout the evolution can be added to the Chromosomes. A weighted fitness function and a multiobjective non-dominated fitness function were tried, and then partial objective ranking was added. The system has tackled a mixture of problems has produced promising results.

show abstract

Allocating Railway Platforms Using A Genetic Algorithm

Cited by 7 publications

References 24 publications

Railway platform reallocation after dynamic perturbations using ant colony optimisation

Railway platform reallocation after dynamic perturbations using ant colony optimisation

Towards Self Sustainable Railway Transportation System!

Train Timetable Generation Using Genetic Algorithms

Contact Info

Product

Resources

About