Skip to main content
SpringerLink
Log in

A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet

  • Workshop on Biologically Inspired Solutions to Parallel Processing Problems Albert Y. Zomaya, The University of Western Australia Fikret Ercal, University of Missouri-Rolla Stephan Olariu, Old Dominion Univesity
  • Conference paper
  • First Online:
Parallel and Distributed Processing (IPPS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1388))

Included in the following conference series:

Abstract

Nowadays genetic algorithms stand as a trend to solve NPcomplete and NP-hard problems. In this paper, we present a new hybrid metaheuristic which uses Parallel Genetic Algorithms and Scatter Search coupled with a decomposition-into-petals procedure for solving a class of Vehicle Routing and Scheduling Problems. The parallel genetic algorithm presented is based on the island model and was run on a cluster of workstations. Its performance is evaluated for a heterogeneous fleet problem, which is considered a problem much harder to solve than the homogeneous vehicle routing problem.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. BALL, M.O.; MAGNANTI, T.L.; MONNA, C.L. and NENHAUSER, G.L., 1995, Network Routing, Handbook in Op. Res. and Manag. Sc., Vol 8, Elsevier Science.

    Google Scholar 

  2. BODIN, L.D.; GOLDEN, L.; ASSAD, A.A. and BALL, M., 1983, Routing and Scheduling of Vehicles and Crews, The State of the Art, Comp. and Op. Res., Vol 10,63–211.

    Google Scholar 

  3. DRUMMOND, L. M. A. and BARBOSA, V. C., 1996, Distributed Breakpoint Detection in Message-passing Programs, J. of Par. and Dist. Comp., 39, 153–167.

    Article  Google Scholar 

  4. GENDRAU, M.; HERTZ, A. and LAPORTE, G., 1992, New Insertion PostOptimization Procedures for the Traveling Salesman Problem, Oper. Res. 40, 1086–1094.

    Google Scholar 

  5. GLOVER, F., 1995, Scatter Search and Star-Paths: Beyond the Genetic Metaphor, OR SPECTRUM, Vol 17, Issue 2/3.

    Google Scholar 

  6. GLOVER, F., 1997, Tabu Search and Adaptive Memory Programming: Advances, Applications and Challenges. Interfaces in Comp. Sc. and Oper. Res., 1–76, Kluwer Academic Publishers.

    Google Scholar 

  7. GOLDEN, B.; ASSAD, A.; LEVY, L. and GHEYSENS, F.G., 1984, The Fleet Size and Mix Vehicle Routing Problem, Comp. and Op. Res., Vol 11, 49–66.

    Article  Google Scholar 

  8. HOLLAND, J.H., 1976, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor.

    Google Scholar 

  9. SNIR, M.; OTTO, S. W.; HUSS-LEDERMAN, S.; WALKER, D. W. and DONGARRA, J., 1996, MPI: The Complete Reference, The MIT Press.

    Google Scholar 

  10. OCHI, L.S.; DRUMMOND, L.M.A. and FIGUEIREDO, R.M., 1997, Design and Implementation of a Parallel Genetic Algorithm for the Traveling Purchaser Problem, ACM Symp. on Applied Computing, 257–263.

    Google Scholar 

  11. OCHI, L.S.; RABELO, P.G. and MALULAN, N., 1997, A New Genetic Metaheuristic for the Clustered Traveling Salesman Problem, II Metaheuristic International Conf, 59–64.

    Google Scholar 

  12. OCHI, L. S., VIANNA, D. S.; DRUMMOND, L. M. A. and VICTOR, A. O., 1998, An Evolutionary Hybrid Metaheuristic for Solving the Vehicle Routing Problem with Heterogeneous Fleet, to appear in Euro. Workshop on Genetic Programming.

    Google Scholar 

  13. REEVES, R., 1993, Modern Heuristic Technics for Combinatorial Problems, Blackwell Scientific Publications.

    Google Scholar 

  14. RIBEIRO, J. L., 1995, An Object-oriented Programming Environment for Parallel Genetic Algorithm, Ph.D. Thesis, Dep. of Comp. Se., Univ. College London.

    Google Scholar 

  15. ROCHAT, Y. and TAILLARD, E.D., 1995, Probabilistic Diversification and Intensification on Local Search for Vehicle Routing, J. of Heuristics, 147–167.

    Google Scholar 

  16. RYAN, D.M.; HJORRING and GLOVER, F., 1993, Extensions of the Petal Method for Vehicle Routing, J.Op.Res., Vol 44(3), 289–296.

    Google Scholar 

  17. TAILLARD, E.D., 1996, A Heuristic Column Generation Method for the Heterogeneous Fleet, Publication CRT-03-96, Université de Montreal.

    Google Scholar 

  18. WHITLEY, D.; STARKWEATHER, T. and SHANER, D., 1991, The traveling Salesman and Sequence Scheduling: Quality Solutions Using Genetic Edge Recombination, Handbook of Genetic Algorithms, Van Nostrand Reinhold, NY.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. PGCC-UFF, Niterói, RJ-Brazil

    Luiz S. Ochi, Dalessandro S. Vianna, Lucia M. A. Drummond & André O. Victor

Authors
  1. Luiz S. Ochi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dalessandro S. Vianna
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lucia M. A. Drummond
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. André O. Victor
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

José Rolim

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ochi, L.S., Vianna, D.S., Drummond, L.M.A., Victor, A.O. (1998). A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet. In: Rolim, J. (eds) Parallel and Distributed Processing. IPPS 1998. Lecture Notes in Computer Science, vol 1388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64359-1_691

Download citation

  • DOI: https://doi.org/10.1007/3-540-64359-1_691

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64359-3

  • Online ISBN: 978-3-540-69756-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation