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Annals of Operations Research

, Volume 271, Issue 2, pp 1165–1183 | Cite as

A greedy approach for a rolling stock management problem using multi-interval constraint propagation

ROADEF/EURO challenge 2014
  • Hugo Joudrier
  • Florence Thiard
Rolling Stock Unit Management

Abstract

In this article we present our contribution to the Rolling Stock Unit Management problem proposed for the ROADEF/EURO Challenge 2014. We propose a greedy algorithm to assign trains to departures. Our approach relies on a routing procedure using multi-interval constraint propagation to compute the individual schedules of trains within the railway station. This algorithm allows to build an initial solution, satisfying a significant subset of departures.

Keywords

Scheduling Railway stock management Greedy algorithm Multi-interval constraint propagation 

References

  1. Benhamou, F., & Granvilliers, L. (2006). Continuous and interval constraints. Foundations of Artificial Intelligence, 2, 571–603.CrossRefGoogle Scholar
  2. Chabert, G., & Jaulin, L. (2009). Contractor programming. Artificial Intelligence, 173(11), 1079–1100.CrossRefGoogle Scholar
  3. Floyd, R. W. (1962). Algorithm 97: Shortest path. Communications of the ACM, 5(6), 345.CrossRefGoogle Scholar
  4. Hudak, P., Hughes, J., Peyton Jones, S. & Wadler, P. (2007). A history of haskell: Being lazy with class. In Proceedings of the Third ACM SIGPLAN Conference on History of Programming Languages (pp. 12-1–12-55). ACM.Google Scholar
  5. ILOG, S. (1999). Revising hull and box consistency. In Logic Programming: Proceedings of the 1999 International Conference on Logic Programming, (p. 230). MIT Press.Google Scholar
  6. Johnsson, T. (1984). Efficient compilation of lazy evaluation. SIGPLAN Notices, 19(6), 58–69.CrossRefGoogle Scholar
  7. Knüppel, O. (1994). Profil/biasa fast interval library. Computing, 53(3–4), 277–287.CrossRefGoogle Scholar
  8. Lerch, M., Tischler, G., Gudenberg, J. W. V., Hofschuster, W., & Krämer, W. (2006). Filib++, a fast interval library supporting containment computations. ACM Transactions on Mathematical Software (TOMS), 32(2), 299–324.CrossRefGoogle Scholar
  9. Madsen, A. L., & Jensen, F. V. (1999). Lazy propagation: A junction tree inference algorithm based on lazy evaluation. Artificial Intelligence, 113(1), 203–245.CrossRefGoogle Scholar
  10. Moore, R. E. (1966). Interval analysis., Prentice-Hall series in automatic computation Englewood Cliffs: Prentice-Hall.Google Scholar
  11. Ninin, J. (2015). Global optimization based on contractor programming: An overview of the ibex library. In International Conference on Mathematical Aspects of Computer and Information Sciences (pp. 555–559). Springer.Google Scholar
  12. Ramond, F., & Marcos, N. (2014). Trains don’t vanish ! ROADEF EURO 2014 challenge problem description. Technical report, SNCF—Innovation & Research Department. https://hal.archives-ouvertes.fr/hal-01057324.
  13. Rogers, D. F., Plante, R. D., Wong, R. T., & Evans, J. R. (1991). Aggregation and disaggregation techniques and methodology in optimization. Operations Research, 39(4), 553–582.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.G-SCOPUniv. Grenoble Alpes, CNRS, Grenoble INPGrenobleFrance

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