Advertisement

Cooperative Ant Colonies for Optimizing Resource Allocation in Transportation

  • Karl Doerner
  • Richard F. Hartl
  • Marc Reimann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

In this paper we propose an ACO approach, where two colonies of ants aim to optimize total costs in a transportation network. This main objective consists of two sub goals, namely fleet size minimization and minimization of the vehicle movement costs, which are conflicting for some regions of the solution space. Thus, our two ant colonies optimize one of these sub-goals each and communicate information concerning solution quality. Our results show the potential of the proposed method.

Keywords

Priority Rule Vehicle Movement Slave Population Pheromone Information Full Truckload 
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.
    Bullnheimer, B., Hartl, R.F. and Strauss, Ch.: An improved ant system algorithm for the vehicle routing problem. Annals of Operations Research 89 (1999) 319–328MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Costa, D. and Hertz, A.: Ants can colour graphs. Journal of the Operational Research Society 48(3) (1997) 295–305CrossRefzbMATHGoogle Scholar
  3. 3.
    Dorigo, M. and Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the Travelling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1) (1997) 53–66CrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Di Caro, G. and Gambardella, L.M.: Ant Algorithms for Discrete Optimization. Artificial Life 5(2) (1999) 137–172CrossRefGoogle Scholar
  5. 5.
    Stützle, T. and Dorigo, M.: ACO Algorithms for the Quadratic Assignment Problem. In: Corne, D., Dorigo, M. and Glover, F. (Eds.): New Ideas in Optimization. Mc Graw-Hill, London (1999)Google Scholar
  6. 6.
    Colorni, A., Dorigo, M. and Maniezzo, V.: Distributed Optimization by Ant Colonies. In: Varela, F. and Bourgine, P. (Eds.): Proc. Europ. Conf. Artificial Life. Elsevier, Amsterdam (1991)Google Scholar
  7. 7.
    Dorigo, M.: Optimization, Learning and Natural Algorithms. Doctoral Dissertation. Politecnico di Milano, Italy (1992)Google Scholar
  8. 8.
    Dorigo, M., Maniezzo, V. and Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man and Cybernetics 26(1) (1996) 29–41CrossRefGoogle Scholar
  9. 9.
    Gutjahr, W.J.: A graph-based Ant System and its convergence. Future Generation Computing Systems. 16 (2000) 873–888CrossRefGoogle Scholar
  10. 10.
    Gambardella, L.M., Taillard, E. and Agazzi, G.: MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. In: Corne, D., Dorigo, M. and Glover, F. (Eds.): New Ideas in Optimization. McGraw-Hill, London (1999)Google Scholar
  11. 11.
    Irnich, St.: A Multi-Depot Pickup and Delivery Problem with a Single Hub and Heterogeneous Vehicles. European Journal of Operational Research 122(2) (2000) 310–328MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Doerner, K.F., Gronalt, M., Hartl, R.F., and Reimann, M.: Optimizing Pickup and Delivery Operations in a Hub Network with Ant Systems. POM Working Paper 07/2000Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Karl Doerner
  • Richard F. Hartl
  • Marc Reimann
    • 1
  1. 1.Institute of Management ScienceUniversity of ViennaViennaAustria

Personalised recommendations