Skip to main content
SpringerLink
Log in
Book cover

Irish Conference on Artificial Intelligence and Cognitive Science

AICS 2002: Artificial Intelligence and Cognitive Science pp 95–102Cite as

GVR: A New Genetic Representation for the Vehicle Routing Problem

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2464))

Abstract

In this paper we analyse a new evolutionary approach to the vehicle routing problem. We present Genetic Vehicle Representation (GVR), a two-level representational scheme designed to deal in an effective way with all the information that candidate solutions must encode. Experimental results show that this method is both effective and robust, allowing the discovery of new best solutions for some well-known benchmarks.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Desrosiers, J., Madsen, O., Solomon, M. and Soumis, F. (1999). 2-Path Cuts for the Vehicle Routing Problem with TimeWindows, Transportation Science, Vol. 33, No. 1, pp. 101–116.

    Article  MATH  Google Scholar 

  2. Thangiah, S., Potvin, J. and Sun, T. (1996). Heuristic Approaches to Vehicle Routing with Backhauls and Time Windows, Int. Journal of Computers and Operations Research, pp. 1043–1057.

    Google Scholar 

  3. Prosser, P. and Shaw, P. (1997). Study of Greedy Search with Multiple Improvement Heuristics for Vehicle Routing Problems, Technical Report RR/96/201, Department of Computer Science, University of Strathclyde, Glasgow.

    Google Scholar 

  4. Tan, K. C., Lee, L. H., Zhu, Q. L. and Ou K. (2000). Heuristic Methods for Vehicle Routing Problem with Time Windows, Artificial Intelligent in Engineering, pp. 281–295.

    Google Scholar 

  5. Bent, R. and Hentenryck, P. V. (2001). A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows, Technical Report, CS-01-06, Brown University.

    Google Scholar 

  6. Shaw, P. (1998). Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems, Proceedings of the Fourth International Conference on Principles and Practice of Constraint Programming (CP’ 98), M. Maher and J. Puget (eds.), pp. 417–431.

    Google Scholar 

  7. Gambardella, L. M., Taillard, E. and Agazzi, G. (1999) MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows, In D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, London, UK, pp. 63–76.

    Google Scholar 

  8. Bräysy, O. (2001). Genetic Algorithm for the Vehicle Routing Problem with Time Windows, Arpakannus 1/2001, Special Issue on Bioinformatics and Genetic Algorithms, pp.33–38.

    Google Scholar 

  9. Thangiah, S. R. (1995). Vehicle Routing with Time Windows Using Genetic Algorithms, Application Handbook of Genetic Algorithms: New Frontiers, Volume II. Chambers, L.(ed), pp. 253–277, CRC Press.

    Google Scholar 

  10. Potvin, J., Dubé, D. and Robillard, C. (1996). Hybrid Approach to Vehicle Routing Using Neural Networks and Genetic Algorithms, Applied Intelligence, Vol. 6, No. 3, pp. 241–252.

    Article  Google Scholar 

  11. Zhu, K. (2000). A New Genetic Algorithm for VRPTW, International Conference on Artificial Intelligence, Las Vegas, USA.

    Google Scholar 

  12. Louis, S. J., Yin, X. and Yuan, Z. Y. (1999). Multiple Vehicle Routing With Time Windows Using Genetic Algorithms, Proceedings of the Congress of Evolutionary Computation (CEC-99), pp. 1804–1808.

    Google Scholar 

  13. Duncan, T. (1995). Experiments in the Use of Neighbourhood Search Techniques for Vehicle Routing. Report AIAI-TR-176, University of Edinburgh.

    Google Scholar 

  14. Machado, P., Tavares, J., Pereira, F. B. and Costa, E. (2002). Vehicle Routing Problem: Doing it the Evolutionary Way, To appear at GECCO-2002 Proceedings.

    Google Scholar 

  15. Available at: http://www.branchandcut.org/VRP/data/.

Download references

Author information

Authors and Affiliations

  1. Instituto Superior de Engenharia de Coimbra, Quinta da Nora, 3030, Coimbra, Portugal

    Francisco B. Pereira & Penousal Machado

  2. Centro de Informática e Sistemas da Universidade de Coimbra, 3030, Coimbra, Portugal

    Francisco B. Pereira, Jorge Tavares, Penousal Machado & Ernesto Costa

Authors
  1. Francisco B. Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge Tavares
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Penousal Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ernesto Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Ireland

    Michael O’Neill , Richard F. E. Sutcliffe , Conor Ryan , Malachy Eaton  & Niall J. L. Griffith , , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pereira, F.B., Tavares, J., Machado, P., Costa, E. (2002). GVR: A New Genetic Representation for the Vehicle Routing Problem. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_12

Download citation

  • DOI: https://doi.org/10.1007/3-540-45750-X_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44184-7

  • Online ISBN: 978-3-540-45750-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation