An Algorithm of Schedule Planning for Tanker Drivers

  • Jerzy Greblicki
  • Jerzy Kotowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5717)

Abstract

In this paper a certain modification of an evolutionary algorithm is characterized, which is intended for planning a schedule for tanker drivers working for a petrol base. The description of a computational algorithm is presented, starting from the description of a particular genotype and phenotype. The most important element of the algorithm is the procedure of projection of the genotype on the set of phenotypes based on the Baldwin Effect. The final part of the paper presents computational tests and plans for the future.

Keywords

cutting stock vehicle routing problem genetic algorithm 

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References

  1. 1.
    Anderson, D., Anderson, E., Lesh, N., Marks, J., Mirtich, B., Ratajczak, D., Ryall, K.: Human-Guided Simple Search, Working Paper, Mitsubishi Electric Research Laboratory, Cambridge, USA (2000)Google Scholar
  2. 2.
    Arabas, J.: Lectures on Evolutionary Algorithms. WNT, Warsaw (2001) (in Polish)Google Scholar
  3. 3.
    Baldwin, J.M.: A new Factor in Evolution. American Naturalist 30, 441–451 (1896)CrossRefGoogle Scholar
  4. 4.
    French, R.M., Messinger, A.: Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population. In: Artificial Live IV, pp. 277–282. MIT Press, Cambridge (1994)Google Scholar
  5. 5.
    Klempous, R., Kotowski, J., Szlachcic, E.: Interactive procedures in large scale two-dimensional cutting stock problems. Journal of CAM 66, 323–332 (1996)MathSciNetMATHGoogle Scholar
  6. 6.
    Kotowski, J.: The use of the method of illusion to optimizing the simple cutting stock problem. In: Proc. MMAR 2001, 7th IEEE Conference on Methods and Models in Automation and Robotics, vol. 1, pp. 149–154 (2001)Google Scholar
  7. 7.
    Laporte, G., Semet, F.: Classical Heuristics for the Vehicle Routing Problem, Les Cahiers de GERAD, G-98-54, 1-19 (1999)Google Scholar
  8. 8.
    Potvin, J.-Y., Robillard, C.: Clustering for Vehicle Routing with a Competitive Neural Network. Neuro-computing 8, 125–139 (1995)Google Scholar
  9. 9.
    Sokołowski, M., Szlachcic, E.: A New Heuristic Algorithm for the Vehicle Routing Problem with Time Windows. In: Proc. MMAR 2001, 7th IEEE Conference on Methods and Models in Automation and Robotics, vol. 1, pp. 1201–1206 (2004)Google Scholar
  10. 10.
    Toth, P., Vigo, D.: The Vehicle Routing Problem. Monographs on Discrete Mathematics and Applications. SIAM, Philadelphia (2001)MATHGoogle Scholar
  11. 11.
    Turney, P.D.: Myths and Legends of the Baldwin Effect. In: Proc. GECCO 1999, Genetic and Evolutionary Computation Conference (1999)Google Scholar
  12. 12.
    Weber, B.H., Depew, D.J.: Evolution and Learning: The Baldwin Effect Reconsidered. MIT Press, Cambridge (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jerzy Greblicki
    • 1
  • Jerzy Kotowski
    • 1
  1. 1.Institute of Computer Engineering, Control and RoboticsWrocław University of TechnologyWrocławPoland

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