Abstract
We consider how to improve railway timetables. As case we take the Rompnet of the dutch railways, a highly constrained problem, containing both hard and soft constraints. We show how to cast the constraints into the format of the Genocop system. Every train should run every hour at the same time and hence the constraints should be interpreted modulo sixty. This gives a non-convex search space of integer vectors. The Genocop system is designed for convex search spaces, but we show how to adapt the Genocop operators to deal with this nonconvex search space. The results of two experiments using the adapted operators are very encouraging.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Abramson, D., Mills, G. Perkins, S. Parallelisation of a Genetic Algorithm for the Computation of Efficient Train Schedules Proceedings of the 1993 Parallel Computing and Transputers Conference, IOS Press, 1993.
Bagchi, S., Uckun, S., Miyabe, Y. & Kawamura, K. Exploring Problem-Specific Recombination Operators for Job Shop Scheduling. Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 11–17, Morgan Kaufmann, 1991.
A. Colorni, M. Dorigo, and V. Manezzo. Genetic algorithms and highly constrained problems — the time-table case. In H.P. Schwefel and R. Männer, editors, Proceedings First Conference on Parallel Problem Solving from Nature, pages 55–59, 1990.
Cleveland G.A. & Smith, S.F. Using Genetic Algorithms to Schedule Flow Shop Releases. Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 161–169, Morgan Kaufmann, 1991.
L. Davis. Handbook of Genetic Algorithms. Van Nostrand Reinhold, 1991.
Gabbert, P.S., Brown, D.E., Huntley, C.L., Markowicz, B.P. & Sappington, D.E. A System for Learning Routes and Schedules with Genetic Algorithms. Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 430–436, Morgan Kaufmann, 1991.
Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag, 1992.
Z. Michalewicz and C.Z. Janikow. Handling constraints in genetic algorithms. In Proceedings of the fourth international Conference on Genetic Algorithms. Morgen Kaufmann Publishers, 1991.
S.R. Thangiah and K.E. Nygard. Schoolbus routing using genetic algorithms. In Proceedings SPIE, Applications of Artificial Intelligence X — Knowledge based systems, volume 1707, pages 387–398, 1993.
D. Whitley, T. Starkweather, and D. Sharon. Using simulations with genetic algorithms for optimizing schedules. In Proceedings Computer Simulation Conference, pages 288–293, 1990.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
van Wezel, M.C., Kok, J.N., van den Berg, J., van Kampen, W. (1994). Genetic improvement of railway timetables. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_299
Download citation
DOI: https://doi.org/10.1007/3-540-58484-6_299
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58484-1
Online ISBN: 978-3-540-49001-2
eBook Packages: Springer Book Archive