Skip to main content

Combination and Comparison of Different Genetic Encodings for the Vehicle Routing Problem

  • Conference paper
Computer Aided Systems Theory – EUROCAST 2011 (EUROCAST 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6927))

Included in the following conference series:

Abstract

Unlike for other problems, such as the traveling salesman problem, no widely accepted encodings for the vehicle routing problem have been developed yet. In this work, we examine different encodings and operations for vehicle routing problems. We show, how different encodings can be combined in one algorithm run and compare the individual encodings in terms of runtime and solution quality. Based on those results, we perform extensive test cases on different benchmark instances and show how the combination of different encodings and operations can be beneficial and provide a balance between solution quality and runtime.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Affenzeller, M., Wagner, S.: Offspring selection: A new self-adaptive selection scheme for genetic algorithms. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds.) Adaptive and Natural Computing Algorithms. Springer Computer Series, pp. 218–221. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  2. Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights), 1st edn. Chapman & Hall, Boca Raton (2009)

    Book  MATH  Google Scholar 

  3. Alba, E., Dorronsoro, B.: Solving the vehicle routing problem by using cellular genetic algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 11–20. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  4. Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows part ii: Metaheuristics. Transportation Science 39, 119–139 (2005)

    Article  Google Scholar 

  5. Cordeau, J.F., Gendreau, M., Hertz, A., Laporte, G., Sormany, J.S.: New heuristics for the vehicle routing problem. In: Logistics Systems: Design and Optimization, New York. ch.9, pp. 279–297 (2005)

    Google Scholar 

  6. Eksioglu, B., Vural, A.V., Reisman, A.: The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering 57(4), 1472–1483 (2009)

    Article  Google Scholar 

  7. Pereira, F., Tavares, J., Machado, P., Costa, E.: 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.) AICS 2002. LNCS (LNAI), vol. 2464, pp. 95–320. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Potvin, J.-Y., Bengio, S.: The vehicle routing problem with time windows -. part ii: Genetic search. INFORMS Journal on Computing 8, 165–172 (1996)

    Article  MATH  Google Scholar 

  9. Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research 31(12), 1985–2002 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  10. Solomon, M.: Algorithms for the Vehicle Routing and Scheduling Problem with Time Window Constraints. Operations Research 35(2), 254–265 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  11. Taillard, E.D.: Benchmarks for basic scheduling problems. European Journal of Operational Research 64, 278–285 (1993)

    Article  MATH  Google Scholar 

  12. Wagner, S.: Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. Ph.D. thesis, Johannes Kepler University, Linz, Austria (2009)

    Google Scholar 

  13. Zhu, K.Q.: A new genetic algorithm for vrptw. In: Proceedings of the International Conference on Artificial Intelligence, p. 311264 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vonolfen, S., Beham, A., Affenzeller, M., Wagner, S., Mayr, A. (2012). Combination and Comparison of Different Genetic Encodings for the Vehicle Routing Problem. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27549-4_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27548-7

  • Online ISBN: 978-3-642-27549-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics