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An Improved Evolutionary Algorithm for Dynamic Vehicle Routing Problem with Time Windows

  • Jiang-qing Wang
  • Xiao-nian Tong
  • Zi-mao Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4490)

Abstract

The dynamic vehicle routing problem is one of the most challenging combinatorial optimization tasks. The interest in this problem is motivated by its practical relevance as well as by its considerable difficulty. We present an approach to search for best routes in dynamic network. We propose a dynamic route evaluation model for modeling the responses of vehicles to changing traffic information, a modified Dijkstra’s double bucket algorithm for finding the real-time shortest paths, and an improved evolutionary algorithm for searching the best vehicle routes in dynamic network. The proposed approach has been evaluated by simulation experiment using DVRPSIM. It has been found that the proposed approach quite efficient in finding real-time best vehicle routes where the customer nodes and network information changes dynamically.

Keywords

Combinatorial Optimization Dynamic Vehicle Routing Problem Dijkstra Algorithm Evolutionary Algorithm 

References

  1. 1.
    Tighe, A., Smith, F.S., Lyons, G.: Priority based solver for a real-time dynamic vehicle routing. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 7, pp. 6237–6242 (2004)Google Scholar
  2. 2.
    Donati, A.V., Montemanni, R., Gambardella, L.M., Rizzoli, A.E.: Integration of a robust shortest path algorithm with a time dependent vehicle routing model and applications. In: IEEE International Symposium on Computational Intelligence for Measurement Systems and Applications, CIMSA ’03, pp. 26–31 (2003)Google Scholar
  3. 3.
    Tan, K.C., Lee, T.H., Chew, Y.H., Lee, L.H.: A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 361–366 (2003)Google Scholar
  4. 4.
    Tan, K.C., Lee, T.H., Ou, K., Lee, L.H.: A messy genetic algorithm for the vehicle routing problem with time window constraints. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 679–686 (2001)Google Scholar
  5. 5.
    Alvarenga, G.B., Mateus, G.R.: A two-phase genetic and set partitioning approach for the vehicle routing problem with time windows. In: Fourth International Conference on Hybrid Intelligent Systems, HIS ’04, pp. 428–433 (2004)Google Scholar
  6. 6.
    Psaraftis, H.N.: Dynamic vehicle routing: Status and prospects. Annals of Operations Research 61, 143–164 (1995)zbMATHCrossRefGoogle Scholar
  7. 7.
    Alvarenga, G.B., de Abreu Silva, R.M., Mateus, G.R.: A hybrid approach for the dynamic vehicle routing problem with time windows. In: 5th International Conference on Hybrid Intelligent Systems (HIS 2005), pp. 61–67 (2005)Google Scholar
  8. 8.
    Song, J., Hu, J., Tian, Y., Xu, Y.: Re-optimization in dynamic vehicle routing problem based on wasp-like agent strategy. In: Proceedings of IEEE Intelligent Transportation Systems, pp. 231–236 (2005)Google Scholar
  9. 9.
    Qiang, L.: Integration of dynamic vehicle routing and microscopic traffic simulation. In: Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, pp. 1023–1027 (2004)Google Scholar
  10. 10.
    Lou, S.Z., Shi, Z.K.: An effective tabu search algorithm for large- scale and real-time vehicle dispatching problems. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3579–3584 (2005)Google Scholar
  11. 11.
    Del Bimbo, A., Pernici, F.: Distant targets identification as an on-line dynamic vehicle routing problem using an active-zooming camera. In: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 97–104 (2005)Google Scholar
  12. 12.
    Tian, Y., Song, J., Yao, D., Hu, J.: Dynamic vehicle routing problem using hybrid ant system. In: Proceedings of IEEE Intelligent Transportation Systems, vol. 2, pp. 970–974 (2003)Google Scholar
  13. 13.
    Ce, F., Hui, W., Ying, Z.: Solving the vehicle routing problem with stochastic demands and customers. In: Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2005, pp. 736–739 (2005)Google Scholar
  14. 14.
    Kim, S., Lewis, M.E., White, C.C.: Optimal vehicle routing with real-time traffic information. IEEE Transactions on Intelligent Transportation Systems 6(2), 178–188 (2005)CrossRefGoogle Scholar
  15. 15.
    Jung, S.J.: A Genetic Algorithm for the Vehicle Routing Problem with Time Dependent Travel Times. PhD thesis, University of Maryland, USA (2000)Google Scholar
  16. 16.
    Fischetti, M., Laporte, G., Mattello, S.: The delivery man problem and cumulative matroids. Operation Research 41, 1055–1076 (1993)zbMATHGoogle Scholar
  17. 17.
    Malandraki, C., Daskin, M.S.: Time dependent vehicle routing problems: Formulations, properties and heuristic algorithms. Transportation Science 26(3), 185–200 (1992)zbMATHCrossRefGoogle Scholar
  18. 18.
    Bean, J.: Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing 6(2), 154–160 (1994)zbMATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jiang-qing Wang
    • 1
  • Xiao-nian Tong
    • 1
  • Zi-mao Li
    • 1
  1. 1.College of Computer Science,South-Central University For Nationalities, Wuhan,430074China

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