Skip to main content
SpringerLink
Log in

Application of Ant Algorithm Variants to Single Track Railway Scheduling Problem

  • Conference paper
  • First Online:
Proceedings of the Third International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 258))

  • 1615 Accesses

Abstract

This paper proposes the methodology to solve the railway time tabling problem using the ant algorithm variants. The aim of the paper is to arrive at the conflict free schedules for the set of trains, considering all the operational constraints. A well defined model is used to solve the scheduling problem on single line track with few parallel lines occurring at frequent periods for crossing purpose. The model makes a realistic assumptions that set of trains will be scheduled in a zone that covers set of cities and scheduling is optimized with respect to number of conflicts. The paper investigates the performance by simulation considering realistic parameter values for both Ant Colony Optimization (ACO) and train scheduling problem. Finally, conclusion is drawn by comparing with other ants algorithm variants.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Caprara, A.M., Kroon, L.G., Monaci, M., Peeters, M., Toth, P.: Passenger railway optimization. Transp. Sci. (Elservier) 14, 129–187 (2007)

    Google Scholar 

  2. Kroon, L., Huisman, D., Abbink, E., Fioole, P., Fischetti, M., Maroti, G., Schrijver, A., Steenbeek, A., Ybema, R.: The new Dutch timetable: the OR revolution. Interfaces 39(1), 6–17 (2009)

    Article  Google Scholar 

  3. Zwaneveld, P.J., Kroon, L.G., van Hoesel, S.P.M.: Routing trains through a railway station based on a node packing model. Eur. J. Oper. Res. 128, 14–33 (2001)

    Article  MATH  Google Scholar 

  4. Raghavendra, G.S., Prasanna Kumar, N.: An ACO framework for Single Track Railway Scheduling Problem. In: Proceedings of Seventh International Conference on Bio-Inspired Computation: Theories and Applications. Advances in Intelligent System and Computing (AISC), vol. 201, pp 39–51. Springer, Gwalior (2013)

    Google Scholar 

  5. Raghavendra, G.S., Prasanna Kumar, N.: On the incorporation of punishment mechanism to Ant System (Unpublished work)

    Google Scholar 

  6. Raghavendra, G.S., Prasanna Kumar, N.: Statistical approach for selecting Elite Ants. Ann. Comput. Sci. Ser. 9(2), 69–90 (2011)

    Google Scholar 

  7. Cordeau, J., Toth, P., Vigo, D.: A survey of optimization models for train routing and scheduling. Transp. Sci. (Elservier) 32(4), 380–404 (1998)

    Google Scholar 

  8. Cai, X., Goh, C.J.: A fast heuristic for the train scheduling problem. Comput. Oper. Res. 21(5), 499–510 (1994)

    Article  MATH  Google Scholar 

  9. Kraay, D., Harker, P., Chen, B.: Optimal pacing of trains in freight railroads. Oper. Res. 39(1), 82–99 (1991)

    Article  Google Scholar 

  10. Higgins, A., Kozan, E., Ferreira, L.: Heuristic techniques for single line train scheduling. J. Heuristics 3, 43–62 (1997)

    Article  MATH  Google Scholar 

  11. Kwan, R.S.K., Mistry, P.: A co-evolutionary algorithm for train timetabling. Research Report Series 13, School of Computing, University of Leeds, Leeds (2003)

    Google Scholar 

  12. Tormos, P., Lova, A., Barber, F., Ingolotti, L., Abril, M., Salido, M.A.: A genetic algorithm for railway scheduling problems. Stud. Comput. Intell. 128, 255–276 (2008)

    Article  Google Scholar 

  13. Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. 26(1), 29–41 (1996)

    Article  Google Scholar 

  14. Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank based version of the Ant System: a computational study. Central Eur J Oper Res Econ 7(1), 25–38 (1999)

    Google Scholar 

  15. Blum, C., Roli, A., Dorigo, M.: HC-ACO: The Hypercube framework for Ant Colony Optimization. IEEE Trans. Syst. Man Cybern. 34(2), 1161–1172 (2004)

    Article  Google Scholar 

  16. Stutzle, T., Hoos, H.H.: MAX—MIN Ant System. Futur. Gener. Comput. Syst. 8(16), 889–914 (2000)

    Google Scholar 

  17. Di Caro, G., Dorigo, M.: Ant Colonies for adaptive routing in packet-switched communication networks. In: Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN V), pp. 673–682 (1998)

    Google Scholar 

  18. Costa, D., Hertz, A.: Ants can colour graphs. J. Oper. Res. Soc. 48, 295–305 (1997)

    MATH  Google Scholar 

  19. Vansteenwegen, P., Souffriau, W., Van Oudheusden, D.: The orienteering problem: a survey. Eur. J. Oper. Res. 209, 110 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. BITS Pilani K. K. Birla Goa Campus, Zuarinagar, Goa, India

    G. S. Raghavendra & N. Prasanna Kumar

Authors
  1. G. S. Raghavendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Prasanna Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Prasanna Kumar .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  4. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer India

About this paper

Cite this paper

Raghavendra, G.S., Prasanna Kumar, N. (2014). Application of Ant Algorithm Variants to Single Track Railway Scheduling Problem. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1771-8_64

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1771-8_64

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1770-1

  • Online ISBN: 978-81-322-1771-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation