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Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems

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  • Ziauddin Ursani

    (School of Engineering and Information Technology, Australian Defence Force Academy, University of New South Wales, Sydney, NSW, Australia)

  • Daryl Essam

    (School of Engineering and Information Technology, Australian Defence Force Academy, University of New South Wales, Sydney, NSW, Australia)

  • David Cornforth

    (School of DCIT, University of Newcastle, Callaghan, Newcastle, NSW, Australia)

  • Robert Stocker

    (School of Engineering and Information Technology, Australian Defence Force Academy, University of New South Wales, Sydney, NSW, Australia)

Abstract

This paper is a continuation of two previous papers where the authors used Genetic Algorithm with automated problem decomposition strategy for small scale capacitated vehicle routing problems (CVRP) and vehicle routing problem with time windows (VRPTW). In this paper they have extended their scheme to large scale capacitated vehicle routing problems by introducing selective search version of the automated problem decomposition strategy, a faster genotype to phenotype translation scheme, and various search reduction techniques. The authors have shown that genetic algorithm used with automated problem decomposition strategy outperforms the GAs applied on the problem as a whole not only in terms of solution quality but also in terms of computational time on the large scale problems.

Suggested Citation

  • Ziauddin Ursani & Daryl Essam & David Cornforth & Robert Stocker, 2013. "Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 4(1), pages 17-38, January.
    • Handle: RePEc:igg:jaec00:v:4:y:2013:i:1:p:17-38
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