From the TSP to the Dynamic VRP: An Application of Neural Networks in Population Based Metaheuristic

  • Amir Hajjam
  • Jean-Charles Créput
  • Abderrafiãa Koukam
Part of the Studies in Computational Intelligence book series (SCI, volume 433)


In this paper, we consider the standard dynamic and stochastic vehicle routing problem (dynamic VRP) where new requests are received over time and must be incorporated into an evolving schedule in real time.We identify the key features which make the dynamic problem different from the static problem. The approach presented to address the problem is a hybrid method which manipulates the self-organizing map (SOM) neural network similarly as a local search into a population based memetic algorithm, it is called memetic SOM. The approach illustrates how the concept of intermediate structure provided by the original SOM algorithm can naturally operate in a dynamic and real-time setting of vehicle routing. A set of operators derived from the SOM algorithm structure are customized in order to perform massive and distributed insertions of transport demands located in the plane. The goal is to simultaneously minimize the route lengths and the customer waiting time. The experiments show that the approach outperforms the operations research heuristics that were already applied to the Kilby et al. benchmark of 22 problems with up to 385 customers, which is one of the very few benchmark sets commonly shared on this dynamic problem. Our approach appears to be roughly 100 times faster than the ant colony algorithm MACS-VRPTW, and at least 10 times faster than a genetic algorithm also applied to the dynamic VRP, for a better solution quality.


Local Search Travel Salesman Problem Travel Salesman Problem Memetic Algorithm Vehicle Rout Problem 
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.
    Bentley, J.-L., Weide, B.W., Yao, A.C.: Optimal expected-time algorithms for closest point problems. ACM Trans. Math. Softw. 6(4), 563–580 (1980)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Bertsimas, D.J., Levi, S.D.: A New Generation of Vehicle Routing Research: Robust Algorithms, Addressing Uncertainty. Operations Research 44(2), 286–304 (1996)zbMATHCrossRefGoogle Scholar
  3. 3.
    Christofides, N., Mingozzi, A., Toth, P.: The vehicle routing problem, pp. 315–338. Wiley (1979)Google Scholar
  4. 4.
    Cochrane, E.M., Beasley, J.E.: The co-adaptive neural network approach to the euclidean travelling salesman problem. Neural Network 16(10), 1499–1525 (2003)CrossRefGoogle Scholar
  5. 5.
    Cordeau, J.-F., Gendreau, M., Hertz, A., Laporte, G.T., Sormany, J.-S.: New heuristics for the vehicle routing problem. In: Langevin, A., Riopel, D. (eds.) Logistics Systems: Design and Optimization, pp. 279–297. Springer, US (2005)CrossRefGoogle Scholar
  6. 6.
    Cordeau, J.-F., Laporte, G., Mercier, A.: A unified tabu search heuristic for vehicle routing problems with time windows. The Journal of the Operational Research Society 52(8), 928–936 (2001)zbMATHGoogle Scholar
  7. 7.
    Metaheuristics in Vehicle Routing. In: Crainic, T.G., Laporte, G. (eds.) Fleet Management and Logistics, pp. 33–56. Kluwer, Boston (1999)Google Scholar
  8. 8.
    Creput, J.-C., Koukam, A.: Clustering and routing as a visual meshing process. Journal of Information and optimization sciences 28(4), 573–601 (2007)zbMATHGoogle Scholar
  9. 9.
    Creput, J.-C., Koukam, A.: Interactive meshing for the design and optimization of bus transportation networks. Journal of Transportation Engineering 133(9), 529–538 (2007)CrossRefGoogle Scholar
  10. 10.
    Creput, J.-C., Koukam, A.: Self-organization in evolution for the solving of distributed terrestrial transportation problems. In: Prasad, B. (ed.) Soft Computing Applications in Industry. STUDFUZZ, vol. 226, pp. 189–205. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Creput, J.-C., Koukam, A.: A memetic neural network for the euclidean traveling salesman problem. Neurocomputing 72, 1250–1264 (2009)CrossRefGoogle Scholar
  12. 12.
    Creput, J.-C., Koukam, A., Hajjam, A.: Self-organizing maps in evolutionary approach for the vehicle routing problem with time windows. International Journal of Computer Science and Network Security 7(1), 103–110 (2007)Google Scholar
  13. 13.
    Creput, J.-C., Koukam, A., Lissajoux, T., Caminada, A.: Automatic mesh generation for mobile network dimensioning using evolutionary approach. IEEE Trans. Evolutionary Computation 9(1), 18–30 (2005)CrossRefGoogle Scholar
  14. 14.
    Creput, J.-C., Koukam, A.: The memetic self-organizing map approach to the vehicle routing problem. Soft Computing - A Fusion of Foundations, Methodologies and Applications 12, 1125–1141 (2008)Google Scholar
  15. 15.
    Dongarra, J.: Performance of various computers using standard linear equations software. Technical Report CS-89-85, Department of Computer Science, University of Tennesse, US (2006)Google Scholar
  16. 16.
    Ergun, O., Orlin, J.B., Steele-Feldman, A.: Creating very large scale neighborhoods out of smaller ones by compounding moves: A study on the vehicle routing problem. MIT Sloan Working Paper No. 4393-02 (October 2002)Google Scholar
  17. 17.
    Gambardella, L.M., Taillard, É., Agazzi, G.: Macs-vrptw: A multiple colony system for vehicle routing problems with time windows. In: New Ideas in Optimization, pp. 63–76. McGraw-Hill (1999)Google Scholar
  18. 18.
    Gendreau, M., Laporte, G., Potvin, J.-Y.: Metaheuristics for the capacitated VRP, pp. 129–154. Society for Industrial and Applied Mathematics, Philadelphia (2001)Google Scholar
  19. 19.
    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 (2003)Google Scholar
  20. 20.
    Glover, F.: Optimization by ghost image processes in neural networks. Computers and Operations Research 21(8), 801–822 (1994); Heuristic, Genetic and Tabu SearchMathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Gonçalves, G., Hsu, T., Dupas, R., Housroum, H.: Une plate-forme de simulation pour la gestion dynamique de tournées de véhicules. Journal Européen des Systèmes Automatisés 41(5), 515–539 (2007)CrossRefGoogle Scholar
  22. 22.
    Helsgaun, K.: An effective implementation of the lin-kernighan traveling salesman heuristic. European Journal of Operational Research 126(1), 106–130 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Johnson, D., McGeoch, L.: Experimental analysis of heuristics for the stsp. In: Du, D.-Z., Pardalos, P.M., Gutin, G., Punnen, A. (eds.) The Traveling Salesman Problem and Its Variations of Combinatorial Optimization, vol. 12, pp. 369–443. Springer, US (2004)CrossRefGoogle Scholar
  24. 24.
    Kilby, P., Prosser, P., Shaw, P.: Dynamic vrps: a study of scenarios. Technical Report APES-06-1998, University of Strathclyde, UK (1998)Google Scholar
  25. 25.
    Kohonen, T.: Self-organization and associative memory, 3rd edn. Springer, New York (1989)CrossRefGoogle Scholar
  26. 26.
    Larsen, A., Madsen, O.B.G., Solomon, M.M.: Recent developments in dynamic vehicle routing systems. In: Sharda, R., Voß, S., Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces Series, vol. 43, pp. 199–218. Springer, US (2008)CrossRefGoogle Scholar
  27. 27.
    Mester, D., Braysy, O.: Active-guided evolution strategies for large-scale capacitated vehicle routing problems. Computers and Operations Research 34(10), 2964–2975 (2007)zbMATHCrossRefGoogle Scholar
  28. 28.
    Montemanni, R., Gambardella, L., Rizzoli, A., Donati, A.: Ant colony system for a dynamic vehicle routing problem. Journal of Combinatorial Optimization 10, 327–343 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Moscato, P.: A gentle introduction to memetic algorithms. In: Handbook of Metaheuristics, pp. 105–144. Kluwer Academic Publishers (2003)Google Scholar
  30. 30.
    Preparata, F.P., Shamos, M.I.: Computational geometry: an Introduction. Springer, New York (1985)Google Scholar
  31. 31.
    Psaraftis, H.N.: Dynamic vehicle routing: Status and prospects. Annals of Operations Research 61, 143–164 (1995)zbMATHCrossRefGoogle Scholar
  32. 32.
    Psaraftis, H.N.: Dynamic vehicle routing problems, pp. 223–248. Elsevier Science Ltd. (1998)Google Scholar
  33. 33.
    Reinelt, G.: Tsplib - a traveling salesman problem library. ORSA Journal on Computing 3(4), 376–384 (1991)zbMATHCrossRefGoogle Scholar
  34. 34.
    Toth, P., Vigo, D.: The granular tabu search and its application to the vehicle-routing problem. INFORMS Journal on Computing 15(4), 333–346 (2003)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amir Hajjam
    • 1
  • Jean-Charles Créput
    • 1
  • Abderrafiãa Koukam
    • 1
  1. 1.Laboratoire Systémes et TransportsU.T.B.M.Belfort CedexFrance

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