Dispatching Strategies for Dynamic Vehicle Routing Problems

  • Besma ZeddiniEmail author
  • Mahdi Zargayouna
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 96)


Online vehicle routing problems are highly complex problems for which several techniques have been successfully proposed. Traditionally, the solutions concern the optimization of conventional criteria (such as the number of mobilized vehicles and the total traveled distance). However, in online systems, the optimization of the response time to the connected users becomes at least as important as the optimization of the traditional criteria. Multi-agent systems and greedy insertion heuristics are the most promising approaches to optimize this criteria. To this end, we propose a multi-agent system and we focus on the clients dispatching strategy. The strategy decides which agents perform the computation to answer the clients requests. We propose three dispatching strategies: centralized, decentralized and hybrid. We compare these three approaches based on their response time to online users. We consider two experiments configuration, a centralized configuration and a network configuration. The results show the superiority of the centralized approach in the first configuration and the superiority of the hybrid approach in the second configuration.


  1. 1.
    Solomon, M.: Algorithms for the vehicle routing and scheduling with time window constraints. Oper. Res. 15, 254–265 (1987)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)CrossRefGoogle Scholar
  3. 3.
    Bessghaier, N., Zargayouna, M., Balbo, F.: Management of urban parking: an agent-based approach. In: International Conference on Artificial Intelligence: Methodology, Systems, and Applications, pp. 276–285. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Diana, M.: The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services. J. Adv. Transp. 40(1), 23–46 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Thangiah, S.R., Shmygelska, O., Mennell, W.: An agent architecture for vehicle routing problems. In: Proceedings of the 2001 ACM Symposium on Applied Computing, SAC 2001, pp. 517–521. ACM Press, New York (2001)Google Scholar
  6. 6.
    Kohout, R., Erol, K.: In-Time agent-based vehicle routing with a stochastic improvement heuristic. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence (AAAI 1999/IAAI 1999), pp. 864–869. AAAI Press, Menlo Park (1999)Google Scholar
  7. 7.
    Zeddini, B., Temani, M., Yassine, A., Ghedira, K.: An agent-oriented approach for the dynamic vehicle routing problem. In: IWAISE 2008, pp. 70–76. IEEE (2008)Google Scholar
  8. 8.
    Zargayouna, M., Balbo, F., Scemama, G.: A multi-agent approach for the dynamic VRPTW. In: ESAW 2008 (2008)Google Scholar
  9. 9.
    Zargayouna, M., Zeddini, B.: Fleet organization models for online vehicle routing problems. In: Transactions on Computational Collective Intelligence VII, pp. 82–102. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Grootenboers, F., de Weerdt, M., Zargayouna, M.: Impact of competition on quality of service in demand responsive transit. In: Dix, J., Witteveen, C. (eds.) MATES 2010. LNCS, vol. 6251, pp. 113–124. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Gendreau, M., Guertin, F., Potvin, J.Y., Taillard, E.D.: Parallel tabu search for real-time vehicle routing and dispatching. Transp. Sci. 33(4), 381–390 (1999)CrossRefGoogle Scholar
  12. 12.
    North, M.J., Howe, T.R., Collier, N.T., Vos, R.J.: The repast simphony runtime system. In: Agent 2005 Conference on Generative Social Processes, Models, and Mechanisms (2005)Google Scholar
  13. 13.
    Zargayouna, M., Zeddini, B., Scemama, G., Othman, A.: Simulating the impact of future internet on multimodal mobility. In: AICCSA 2014. IEEE Computer Society (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Quartz, EISTICergy PontoiseFrance
  2. 2.Université Paris-Est, IFSTTAR, GRETTIAMarne la Vallée Cedex 2France

Personalised recommendations