Central European Journal of Operations Research

, Volume 16, Issue 4, pp 331–343 | Cite as

An algorithm for the capacitated vehicle routing problem with route balancing

Original Paper

Abstract

In this paper, we present a multi-objective evolutionary algorithm for the capacitated vehicle routing problem with route balancing. The algorithm is based on a formerly developed multi-objective algorithm using an explicit collective memory method, namely the extended virtual loser (EVL). We adapted and improved the algorithm and the EVL method for this problem. We achieved good results with this simple technique. In case of this problem the quality of the results of the algorithm is similar to that of other evolutionary algorithms.

Keywords

Evolutionary algorithm Multi-objective optimization Explicit collective memory Combinatorial optimization CVRP 

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References

  1. Berger J, Barkaoui M (2003) A hybrid genetic algorithm for the capacitated vehicle routing problem. In: Cantú-Paz E et al. (eds) GECCO 2003. Lecture notes in computer science, vol 2723. Springer, Berlin, pp 646–656Google Scholar
  2. Borgulya I (2005) A multi-objective evolutionary algorithm with a separate archive. Central Eur J Oper Res 13(3): 233–254Google Scholar
  3. Borgulya I (2006) An evolutionary algorithm for the biobjective QAP. In: Reusch B (eds) Computational intelligence, theory and applications advances in soft computing. Springer, Berlin, pp 577–586CrossRefGoogle Scholar
  4. Borgulya I (2007) An EC-memory based method for the multi-objective TSP. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation GECCO ’07. ACM Press, New York, pp 903Google Scholar
  5. Chen A, Yang G, Wu Z (2006) Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem. J Zhejiang Univ Sci A 7(4): 607–614CrossRefGoogle Scholar
  6. Chitty DM, Hernandez ML (2004) A hybrid ant colony optimisation technique for dynamic vehicle routing. In: Deb K et al. (eds) GECCO 2004, Lecture notes in computer science, vol 3102. Springer, Berlin, pp 48–59Google Scholar
  7. Cordeau J, Laporte G (2002) Tabu search heuristics for the vehicle routing problem. Les Cahiers du GERAD G 2002-15Google Scholar
  8. Czarnas P, Zbigniew J, Czech ZJ, Gocyla P (2004) Parallel simulated annealing for bicriterion optimization problems. In: Wyrzykowski R et al. (eds) PPAM 2003, Lecture notes in computer science, vol 3019. Springer, Berlin, pp 233–240Google Scholar
  9. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkGoogle Scholar
  10. Deb K, Agrawal S, Pratab A, Meyunivan T (2002) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. IEEE Trans Evol Comput 182–197Google Scholar
  11. El-Sherbeny N (2001) Resolution of a vehicle routing problem with multi-objective simulated annealing method. Ph.D. thesis, Facultie Polytechnique de MonsGoogle Scholar
  12. Ganesh K, Narendran TT (2007) CLOVES: a cluster-and-search heuristic to solve the vehicle routing problem with delivery and pick-up. Eur J Oper Res 178(3): 699–717CrossRefGoogle Scholar
  13. Goldberg DE (1989) Genetic algorithm for search, optimizatin and machine learning. Addison-Westley, Reading, MAGoogle Scholar
  14. Hadjiconstantinou E, Christofides N (1995) A new exact algorithm for the vehicle muting problem based on q-paths and k-shortest paths relaxations. Ann Oper Res 61: 21–43CrossRefGoogle Scholar
  15. Jozefowiez N, Semet F, Talbi E (2002) Parallel and hybrid models for multi-objective optimization: application to the vehicle routing problem. In: Merelo Guervos JJ et al. (eds) PPSN VII, Lecture notes in computer science, vol 2439. Springer, Berlin, pp 271–280Google Scholar
  16. Jozefowiez N, Semet F, Talbi E (2006) Enhancements of NSGA II and its application to the vehicle routing problem with route balancing. In: Talbi E et al. (eds) Evolutionary algorithms, 2005. Lecture notes in computer science, vol 3871. Springer, Berlin, pp 131–142Google Scholar
  17. Laporte G (1992) The vehicle routing problem: an overview of exact and approximate algorithms. Eur J Oper Res 59(3): 345–358CrossRefGoogle Scholar
  18. Mazzeo S, Loiseau I (2004) An ant colony algorithm for the capacitated vehicle routing. Electron Notes Discrete Math 18: 181–186CrossRefGoogle Scholar
  19. Montemanni R, Gambardella LM, Rizzoli AE, Donati AV (2002) A new algorithm for a dynamic vehicle routing problem based on ant colony system. Technical Report IDSIA-23-02, IDSIA, November 2002. ftp://ftp.idsia.ch/pub/techrep/IDSIA-23-02.pdf.gz
  20. Pasia JM, Doerner KF, Hartl RF, Reimann M (2007a) A population-based local search for solving a Bi-objective vehicle routing problem. In: Cotta, van Hemert J (eds) EvoCOP 2007, Lecture notes in computer science, vol 4446, pp 166–175, 2007Google Scholar
  21. Pasia JM, Doerner KF, Hartl RF, Reimann M (2007b) Solving a bi-objective vehicle routing problem by pareto-ant colony optimization. In: Stutzle T, Birattari M, Hoos HH (eds) SLS 2007, Lecture notes in computer science, vol 4638, pp 187–191Google Scholar
  22. Prins C (2004) A simple and effective evolutionary algorithm for the vehicle routing problem. Comput Oper Res 31: 1985–2002CrossRefGoogle Scholar
  23. Ribeiro R, Lourenço HR (2001) A multi-objective model for a multi period distribution management problem. In: MIC’2001, pp 97–102Google Scholar
  24. Russel M, Lamont GB (2005) A genetic algorithm for unmanned aerial vehicle rouiting GECCO’05 Washington, DC, USA, ACM 2005, pp 1523–1530Google Scholar
  25. Sun H, Xie J, Xue Y (2005) A sweep-based TCNN algorithm for capacity vehicle routing problem. Lecture notes in computer science, vol 3496. Springer, Berlin, pp 756–761Google Scholar
  26. Tavares J, Pereira FB, Machado P, Costa E (2003) On the influence of GVR in vehicle routing. In: Proceedings of the 2003 ACM symposium on applied computing, pp 753–758, Melbourne, Florida, USA, March 2003Google Scholar
  27. Van Breedam A (2001) Comparing descent heuristics and metaheuristics for the vehicle routing problem. Comput Oper Res 28: 289–315CrossRefGoogle Scholar
  28. Zitzler E, Thiele L, Laumanns M, Fonseca CM, de Fonseca G (2002) Performance assessment of multiobjective optimizations: an analysis and rewiew. IEEE Trans Evol Comput 7(2): 117–132CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Faculty of Business and EconomicsUniversity of PécsPecsHungary

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