Advertisement

Annals of Operations Research

, Volume 89, Issue 0, pp 319–328 | Cite as

An improved Ant System algorithm for theVehicle Routing Problem

  • B. Bullnheimer
  • R.F. Hartl
  • C. Strauss
Article

Abstract

The Ant System is a distributed metaheuristic that combines an adaptive memory with alocal heuristic function to repeatedly construct solutions of hard combinatorial optimizationproblems. In this paper, we present an improved ant system algorithm for the Vehicle RoutingProblem with one central depot and identical vehicles. Computational results on fourteenbenchmark problems from the literature are reported and a comparison with five othermetaheuristic approaches for solving Vehicle Routing Problems is given.

Keywords

Computational Result Heuristic Function Route Problem System Algorithm Central Depot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    B. Bullnheimer, R.F. Hartl and C. Strauss, A new rank based version of the ant system: A computational study, Working Paper No. 1, SFB Adaptive Information Systems and Modelling in Economics and Management Science, Vienna, 1997, to appear in CEJORGoogle Scholar
  2. [2]
    B. Bullnheimer, R.F. Hartl and C. Strauss, Applying the Ant System to the vehicle routing problem, in: Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, eds. S. Voss, S. Martello, I.H. Osman and C. Roucairol, Kluwer, Boston, 1998.Google Scholar
  3. [3]
    B. Bullnheimer, G. Kotsis and C. Strauss, Parallelization strategies for the Ant System, in: High Performance Algorithms and Software in Nonlinear Optimization, eds. R. De Leone, A. Murli, P.M. Pardalos and Toraldo, Kluwer Academic, Dordrecht, 1998.Google Scholar
  4. [4]
    N. Christofides, A. Mingozzi and P. Toth, The Vehicle Routing Problem, in: Combinatorial Optimization, eds. N. Christofides, A. Mingozzi, P. Toth and C. Sandi, Wiley, Chichester, 1979.Google Scholar
  5. [5]
    G. Clarke and J.W. Wright, Scheduling of vehicles from a central depot to a number of delivery points, Oper. Res. 12(1964)568.Google Scholar
  6. [6]
    A. Colorni, M. Dorigo and V. Maniezzo, Distributed optimization by ant colonies, in: Proceedings of the European Conference on Artificial Life, eds. F. Varela and P. Bourgine, Elsevier, Amsterdam, 1991.Google Scholar
  7. [7]
    A. Colorni, M. Dorigo, V. Maniezzo and M. Trubian, Ant System for job-shop scheduling, Belgian Journal of Operations Research, Statistics and Computer Science 34(1994)39.Google Scholar
  8. [8]
    D. Costa and A. Hertz, Ants can colour graphs, J. Oper. Res. Soc. 48(1997)295.Google Scholar
  9. [9]
    G.A. Croes, A method for solving traveling salesman problems, Oper. Res. 6(1958)791.Google Scholar
  10. [10]
    M. Dorigo, Optimization, learning and natural algorithms, Doctoral Dissertation, Politecnico di Milano, Italy, 1992 (in Italian).Google Scholar
  11. [11]
    M. Dorigo and L.M. Gambardella, Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput. 1(1997)53.Google Scholar
  12. [12]
    M. Dorigo, V. Maniezzo and A. Colorni, Ant System: Optimization by a colony of cooperating agents, IEEE Trans. Sys., Man, Cybernetics 26(1996)29.Google Scholar
  13. [13]
    M. Gendreau, A. Hertz and G. Laporte, A tabu search heuristic for the Vehicle Routing Problem, Manag. Sci. 40(1994)1276.Google Scholar
  14. [14]
    H. Ghaziri, Supervision in the self-organizing feature map: Application to the Vehicle Routing Problem, in: Meta-Heuristics: Theory and Applications, eds. I. Osman and J. Kelly, Kluwer, Boston, 1996.Google Scholar
  15. [15]
    B.E. Gillett and L.R. Miller, A Heuristic algorithm for the Vehicle Dispatch Problem, Oper. Res. 22(1974)340.Google Scholar
  16. [16]
    H. Kopfer, G. Pankratz and E. Erkens, Entwicklung eines hybriden Genetischen Algorithmus zur Tourenplanung, Oper. Res. Spekt. 16(1994)21.Google Scholar
  17. [17]
    S. Lin and B.W. Kernighan, An effective heuristic algorithm for the Traveling Salesman Problem, Oper. Res. 21(1993)498.Google Scholar
  18. [18]
    V. Maniezzo, A. Colorni and M. Dorigo, The Ant System applied to the Quadratic Assignment Problem, Technical Report IRIDIA/94-28, Université Libre de Bruxelles, 1994.Google Scholar
  19. [19]
    I. Osman, Metastrategy simulated annealing and tabu search algorithms for the Vehicle Routing Problem, Ann. Oper. Res. 41(1993)421.Google Scholar
  20. [20]
    H. Paessens, The savings algorithm for the Vehicle Routing Problem, Eur. J. Oper. Res. 34(1988)336.Google Scholar
  21. [21]
    C. Rego and C. Roucairol, A parallel tabu search algorithm using ejection chains for the Vehicle Routing Problem, in: Meta-Heuristics: Theory and Applications, eds. I. Osman and J. Kelly, Kluwer, Boston, 1996.Google Scholar
  22. [22]
    Y. Rochat and E.D. Taillard, Probabilistic diversification and intensification in local search for vehicle routing, J. Heuristics 1(1995)147.Google Scholar
  23. [23]
    T. Stuetzle and H. Hoos, The MAX-MIN Ant System and local search for the Traveling Salesman Problem, in: Proceedings of the ICEC'97-IEEE 4th International Conference on Evolutionary Computation, 1997, p. 308.Google Scholar
  24. [24]
    E. Taillard, Parallel iterative search methods for Vehicle Routing Problems, Networks 23(1993)661.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • B. Bullnheimer
  • R.F. Hartl
  • C. Strauss

There are no affiliations available

Personalised recommendations