About this Journal Submit a Manuscript Table of Contents
Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 325686, 9 pages
http://dx.doi.org/10.1155/2013/325686
Research Article

Multiobjective Vehicle Routing Problem with Route Balance Based on Genetic Algorithm

1Institute of Mechanical Engineering, Hubei University of Technology, Wuhan, Hubei 430068, China
2Nanjing University of Information Science and Technology, No. 219, Ningliu Road, Nanjing, Jiangsu 210094, China

Received 2 July 2013; Revised 1 October 2013; Accepted 13 November 2013

Academic Editor: Tinggui Chen

Copyright © 2013 Wei Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. W. P. Savelsbergh, “Local search in routing problems with time windows,” Annals of Operations Research, vol. 4, no. 1, pp. 285–305, 1985. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. N. R. Achuthan, L. Caccetta, and S. P. Hill, “An improved branch-and-cut algorithm for the capacitated vehicle routing problem,” Transportation Science, vol. 37, no. 2, pp. 153–169, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. A. W. J. Kolen, A. H. G. Rinnooy Kan, and H. W. J. M. Trienekens, “Vehicle routing and scheduling with time windows,” Operations Research, vol. 35, no. 2, pp. 266–273, 1987. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  4. M. Desrochers, J. Desrosiers, and M. Solomon, “A new optimization algorithm for the vehicle routing problem with time windows,” Operations Research, vol. 40, no. 2, pp. 342–354, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  5. N. Kohl, Exact methods for time constrained routing and related scheduling problems [Ph.D. thesis], Department of Mathematical Modeling, Technical University of Denmark, 1995.
  6. K. C. Tan, L. H. Lee, Q. L. Zhu, and K. Ou, “Heuristic methods for vehicle routing problem with time windows,” Artificial Intelligence in Engineering, vol. 15, no. 3, pp. 281–295, 2001. View at Publisher · View at Google Scholar · View at Scopus
  7. W. Chiang and R. A. Russell, “Simulated annealing metaheuristics for the vehicle routing problem with time windows,” Annals of Operations Research, vol. 63, pp. 3–27, 1996. View at Scopus
  8. E. D. Taillard, P. Badeau, M. Gendreau, F. Guertin, and J. Potvin, “A tabu search heuristic for the vehicle routing problem with soft time windows,” Transportation Science, vol. 31, no. 2, pp. 170–186, 1997. View at Scopus
  9. S. Thangiah, “Vehicle routing with time windows using genetic algorithms,” in Applications Handbook of Genetic Algorithms: New Frontiers, Volume II, pp. 253–277, CRC Press, Boca Raton, Fla, USA, 1995.
  10. K. C. Tan, L. H. Lee, and K. Ou, “Hybrid genetic algorithms in solving vehicle routing problems with time window constraints,” Asia-Pacific Journal of Operational Research, vol. 18, no. 1, pp. 121–130, 2001. View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  11. G. B. Alvarenga, G. R. Mateus, and G. de Tomi, “A genetic and set partitioning two-phase approach for the vehicle routing problem with time windows,” Computers and Operations Research, vol. 34, no. 6, pp. 1561–1584, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. S. R. Thangiah, “A hybrid genetic algorithms, simulated annealing and tabu search heuristic for vehicle routing problems with time windows,” in Practical Handbook of Genetic Algorithms Complex Structures, Volume 3. L. Chambers, pp. 374–381, CRC Press, 1999.
  13. D. E. Goldberg, Genetic Algorithms Insearch, Optimization and Machine Learning, New York, NY, USA, Addison-Wesley edition, 1989.
  14. K. C. Tan, Y. H. Chew, and L. H. Lee, “A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows,” Computational Optimization and Applications, vol. 34, no. 1, pp. 115–151, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. N. Jozefowiez, F. Semet, and E. Talbi, “Multi-objective vehicle routing problems,” European Journal of Operational Research, vol. 189, no. 2, pp. 293–309, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  16. N. Jozefowiez, F. Semet, and E. Talbi, “Target aiming Pareto search and its application to the vehicle routing problem with route balancing,” Journal of Heuristics, vol. 13, no. 5, pp. 455–469, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Ghoseiri and S. F. Ghannadpour, “Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm,” Applied Soft Computing Journal, vol. 10, no. 4, pp. 1096–1107, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Kuo, “Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem,” Computers and Industrial Engineering, vol. 59, no. 1, pp. 157–165, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. G. B. Dantzig and J. H. Ramser, “The truck dispatching problem,” Management Science, vol. 6, pp. 80–91, 1959. View at MathSciNet
  20. M. A. Figliozzi, “An iterative route construction and improvement algorithm for the vehicle routing problem with soft time windows,” Transportation Research C, vol. 18, no. 5, pp. 668–679, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Pang, “An adaptive parallel route construction heuristic for the vehicle routing problem with time windows constraints,” Expert Systems with Applications, vol. 38, no. 9, pp. 11939–11946, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Baños, J. Ortega, C. Gil, A. L. Marquez, and F. de Toro, “A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows,” Computers and Industrial Engineering, vol. 65, no. 2, pp. 286–296, 2013.
  23. J. O. Baños, C. Gil, A. Fernández, and F. de Toro, “A simulated annealing-based parallel multi-objective approach to vehicle routing problems with time windows,” Expert Systems with Applications, vol. 40, pp. 1696–1707, 2013.
  24. S. R. Thangiah, K. E. Nygard, and P. L. Juell, “GIDEON: a genetic algorithm system for vehicle routing with time windows,” in Proceedings of the 7th IEEE Conference on Artificial Intelligence Applications, pp. 322–328, Miami Beach, Fla, USA, February 1991. View at Scopus
  25. B. Ombuki, B. J. Ross, and F. Hanshar, “Multi-objective genetic algorithm for vehicle routing problem with time windows,” Applied Intelligence, vol. 24, pp. 17–33, 2006.
  26. C. C. Lu and V. F. Yu, “Data envelopment analysis for evaluating the efficiency of genetic algorithms on solving the vehicle routing problem with soft time windows,” Computers & Industrial Engineering, vol. 63, no. 2, pp. 520–529, 2012.
  27. T. Murata and H. Ishibuchi, “Performance evaluation of genetic algorithms for flowshop scheduling problems,” in Proceedings of the 1st IEEE Conference on Evolutionary Computation, pp. 812–817, Orlando, Fla, USA, June 1994. View at Scopus
  28. I. Oliver, D. Smith, and J. Holland, “A study of permutation crossover operators on the traveling salesman problem,” in Proceedings of the 2nd International Conference on Genetic Algorithms and Their Applications, Lawrence Eribaum Associates, Cambridge, Mass, USA, 1987.
  29. M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization, John Wiley & Sons, New York, NY, USA, 2000.
  30. M. M. Solomon, “Algorithms for the vehicle routing and scheduling problems with time window constraints,” Operations Research, vol. 35, no. 2, pp. 254–265, 1987. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet