Computationally Intelligent Online Dynamic Vehicle Routing by Explicit Load Prediction in an Evolutionary Algorithm

  • Peter A. N. Bosman
  • Han La Poutré
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


In this paper we describe a computationally intelligent approach to solving the dynamic vehicle routing problem where a fleet of vehicles needs to be routed to pick up loads at customers and drop them off at a depot. Loads are introduced online during the actual planning of the routes. The approach described in this paper uses an evolutionary algorithm (EA) as the basis of dynamic optimization. For enhanced performance, not only are currently known loads taken into consideration, also possible future loads are considered. To this end, a probabilistic model is built that describes the behavior of the load announcements. This allows the routing to make informed anticipated moves to customers where loads are expected to arrive shortly. Our approach outperforms not only an EA that only considers currently available loads, it also outperforms a recently proposed enhanced EA that performs anticipated moves but doesn’t employ explicit learning. Our final conclusion is that under the assumption that the load distribution over time shows sufficient regularity, this regularity can be learned and exploited explicitly to arrive at a substantial improvement in the final routing efficiency.


Evolutionary Algorithm Dynamic Vehicle Action List Dynamic Optimization Problem Time Spread 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, T.W.: An Introduction to Multivariate Statistical Analysis. John Wiley & Sons Inc., New York (1958)MATHGoogle Scholar
  2. 2.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)MATHGoogle Scholar
  3. 3.
    Bent, R., van Hentenryck, P.: Scenario–based planning for partially dynamic vehicle routing with stochastic customers. Operations Research 52, 977–987 (2004)MATHCrossRefGoogle Scholar
  4. 4.
    Bosman, P.A.N.: Learning, anticipation and time–deception in evolutionary online dynamic optimization. In: Yang, S., Branke, J. (eds.) Proceedings of the Evolutionary Algorithms for Dynamic Optimization Problems EvoDOP Workshop at the Genetic and Evolutionary Computation Conference — GECCO 2005, pp. 39–47. ACM Press, New York (2005)CrossRefGoogle Scholar
  5. 5.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Norwell, Massachusetts (2001)Google Scholar
  6. 6.
    Ghiani, G., Guerriero, F., Laporte, G., Musmanno, R.: Real–time vehicle routing: Solution concepts, algorithms and parallel computing strategies. European Journal of Operational Research 151(1), 1–11 (2004)CrossRefGoogle Scholar
  7. 7.
    Grötschel, M., Krumke, S.O., Rambau, J.: Online optimization of large scale systems. Springer, Berlin (2001)MATHGoogle Scholar
  8. 8.
    Ichoua, S., Gendreau, M., Potvin, J.-Y.: Exploiting knowledge about future demands for real–time vehicle dispatching. Forthcoming in Transportation Science (2006)Google Scholar
  9. 9.
    Laporte, G., Louveaux, F., Mercure, H.: The vehicle routing problem with stochastic travel times. Transportation science 26, 161–170 (1992)MATHCrossRefGoogle Scholar
  10. 10.
    Mitrovic-Minic, S., Krishnamurti, R., Laporte, G.: Double–horizon based heuristics for the dynamic pickup and delivery problem with time windows. Transportation Science B 38, 669–685 (2004)CrossRefGoogle Scholar
  11. 11.
    Mitrovic-Minic, S., Laporte, G.: Waiting strategies for the dynamic pickup and delivery problem with time windows. Transportation Science B 38, 635–655 (2004)CrossRefGoogle Scholar
  12. 12.
    Solomon, M.: The vehicle routing problem and scheduling problems with time window constraints. Operations Research 35, 254–265 (1987)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Tatsuoka, M.M.: Multivariate Analysis: Techniques for Educational and Psychological Research. John Wiley & Sons Inc., New York (1971)MATHGoogle Scholar
  14. 14.
    van Hemert, J.I., La Poutré, J.A.: Dynamic routing problems with fruitful regions: Models and evolutionary computation. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 690–699. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter A. N. Bosman
    • 1
  • Han La Poutré
    • 1
  1. 1.Centre for Mathematics and Computer ScienceAmsterdamThe Netherlands

Personalised recommendations