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Computational Science, Engineering & Technology Series
ISSN 1759-3158
Edited by: Y. Tsompanakis, P. Iványi and B.H.V. Topping
Chapter 9

A Genetic Algorithm based Decision Support System for Railway Track Maintenance and Renewal Management

H. Guler

Faculty of Engineering, Karlsruhe University of Applied Sciences, Germany

Full Bibliographic Reference for this chapter
H. Guler, "A Genetic Algorithm based Decision Support System for Railway Track Maintenance and Renewal Management", in Y. Tsompanakis, P. Iványi and B.H.V. Topping, (Editors), "Civil and Structural Engineering Computational Methods", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 9, pp 171-183, 2013. doi:10.4203/csets.32.9
Keywords: genetic algorithms, decision support systems, railway track maintenance and renewal.

This paper describes a genetic algorithm based decision support system approach for railway track maintenance and renewal management system to analyse the track components and to suggest methods for helping the track managers and engineers. Genetic algorithms, which are strong adaptive optimization methods based on biological principles, were used to find optimized railway track maintenance and renewal (M&R) works. Genetic algorithms (GAs) are a kind of numerical optimisation algorithm inspired by both natural selection and genetic recombination. In this paper, interviews with track maintenance experts and a comprehensive literature survey were used to develop a decision support system, including some decision rules on track M&R. Based on these decision rules, the genetic algorithm techniques were used at the optimisation stage and reasonable results were found for optimized track M&R plans. The developed method is based on replacing more expensive track M&R plans with ones that are less expensive but similar to the original M&R plans. Finally, a network-scale assessment and consequential M&R needs were obtained by using GAs' techniques. Results of this study showed that with an optimal choice of population size and time, a best solution with the given constraints could be achieved in the means of maintenance and renewal of the railway tracks. Consequently, it was observed that more accurate solutions can be achieved with the increase of the sensitivity of the reference data.

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