Evolutionary Freight Transportation Planning

  • Thomas Weise
  • Alexander Podlich
  • Kai Reinhard
  • Christian Gorldt
  • Kurt Geihs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)


In this paper, we present the freight transportation planning component of the INWEST project. This system utilizes an evolutionary algorithm with intelligent search operations in order to achieve a high utilization of resources and a minimization of the distance travelled by freight carriers in real-world scenarios. We test our planner rigorously with real-world data and obtain substantial improvements when compared to the original freight plans. Additionally, different settings for the evolutionary algorithm are studied with further experiments and their utility is verified with statistical tests.


Genetic Algorithm Evolutionary Algorithm Solution Candidate Project Partner Tabu Search Heuristic 
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.


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  1. 1.
    Bundesministerium für Verkehr, Bau- und Stadtentwicklung: Verkehr in Zahlen 2006/2007. Deutscher Verkehrs-Verlag GmbH, Hamburg, Germany (2006)Google Scholar
  2. 2.
    Ceollo Coello, C.A., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  3. 3.
    CEN/TC 119: Swap bodies – non-stackable swap bodies of class C – dimensions and general requirements. EN 284, CEN-CEN ELEC, Brussels, Belgium (2006)Google Scholar
  4. 4.
    Glover, F.: Future paths for integer programming and links to artificial intelligence. Computers & Operations Research 13(5), 533–549 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Amberg, A., Domschke, W., Voß, S.: Multiple center capacitated arc routing problems: A tabu search algorithm using capacitated trees. European Journal of Operational Research (EJOR) 124(2), 360–376 (2000)CrossRefzbMATHGoogle Scholar
  6. 6.
    Badeau, P., Gendreau, M., Guertin, F., Potvin, J.Y., Taillard, É.D.: A parallel tabu search heuristic for the vehicle routing problem with time windows. Transportation Research Part C: Emerging Technologies 5(2), 109–122 (1997)CrossRefzbMATHGoogle Scholar
  7. 7.
    Bräysy, O., Gendreau, M.: Tabu search heuristics for the vehicle routing problem with time windows. TOP: An Official Journal of the Spanish Society of Statistics and Operations Research 10(2), 211–237 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Breedam, A.V.: An analysis of the behavior of heuristics for the vehicle routing problem for a selection of problems with vehicle-related, customer-related, and time-related constraints. PhD thesis, University of Antwerp, Belgium (1994)Google Scholar
  9. 9.
    Czech, Z.J., Czarnas, P.: Parallel simulated annealing for the vehicle routing problem with time windows. In: 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing (PDP 2002), pp. 376–383. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  10. 10.
    Bullnheimer, B., Hartl, R.F., Strauss, C.: An improved ant system algorithm for the vehicle routing problem. Annals of Operations Research 89, 319–328 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Doerner, K., Gronalt, M., Hartl, R.F., Reimann, M., Strauss, C., Stummer, M.: Savings Ants for the vehicle routing problem. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 11–20. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Jih, W., Hsu, J.: Dynamic vehicle routing using hybrid genetic algorithms. In: IEEE International Conference on Robotics and Automation, pp. 453–458 (1999)Google Scholar
  13. 13.
    Thangiah, S.R.: Vehicle routing with time windows using genetic algorithms. In: Practical Handbook of Genetic Algorithms: New Frontiers, pp. 253–277. CRC, Boca Raton (1995)Google Scholar
  14. 14.
    Zhu, K.Q.: A diversity-controlling adaptive genetic algorithm for the vehicle routing problem with time windows. In: 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 176–183. IEEE Computer Society, Los Alamitos (2003)CrossRefGoogle Scholar
  15. 15.
    Alba, E., Dorronsoro, B.: Solving the vehicle routing problem by using cellular genetic algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 11–20. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Ralphs, T.: Vehicle routing data sets, Data sets (2003) (accessed 2008-10-27),
  17. 17.
    Pankratz, G., Krypczyk, V.: Benchmark data sets for dynamic vehicle routing problems (2007) (2008-10-27),
  18. 18.
    Weise, T., Zapf, M., Chiong, R., Nebro, A.J.: Why is optimization difficult? In: Nature-Inspired Algorithms for Optimisation. Springer, Heidelberg (to appear, 2009)Google Scholar
  19. 19.
    Radcliffe, N.J.: The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence 10(4), 339–384 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Weise, T.: Global Optimization Algorithms – Theory and Application, 2nd edn. (2009) (accessed 2009-02-10),
  21. 21.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)zbMATHGoogle Scholar
  22. 22.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  23. 23.
    Podlich, A.: Intelligente Planung und Optimierung des Güterverkehrs auf Straße und Schiene mit evolutionären Algorithmen. Master’s thesis, Univ. of Kassel (2009)Google Scholar
  24. 24.
    Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery. John Wiley & Sons, Chichester (2005)zbMATHGoogle Scholar
  25. 25.
    Yates, F.: The Design and Analysis of Factorial Experiments. Imperial Bureau of Soil Science, Commonwealth Agricultural Bureaux, Tech. Comm. No. 35 (1937)Google Scholar
  26. 26.
    Siegel, S., Castellan Jr., N.J.: Nonparametric Statistics for The Behavioral Sciences. Humanities/Social Sciences/Languages. McGraw-Hill, New York (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas Weise
    • 1
  • Alexander Podlich
    • 2
  • Kai Reinhard
    • 2
  • Christian Gorldt
    • 3
  • Kurt Geihs
    • 1
  1. 1.Distributed Systems GroupUniversity of KasselKasselGermany
  2. 2.Micromata GmbH KasselKasselGermany
  3. 3.BIBA – Bremer Institut für Produktion und Logistik GmbHGermany

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