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A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods


  • Ferrucci, Francesco
  • Bock, Stefan
  • Gendreau, Michel


This paper proposes a new pro-active real-time control approach for dynamic vehicle routing problems in which the urgent delivery of goods is of utmost importance. Without assuming any distribution, stochastic knowledge about future requests is generated using past request information. The generated knowledge is integrated into the transportation process, which is controlled by a Tabu Search algorithm, in order to actively guide vehicles to request-likely areas before requests arrive there. By analyzing the results attained for various test settings, we identify structural diversity as a crucial criterion for classifying the quality of stochastic knowledge attainable from past request information. This criterion provides a promising starting point for assessing the quality of past request information in order to efficiently use the derived stochastic knowledge in real-time control approaches. We prove the efficiency of our approach by a direct comparison with a deterministic approach on test scenarios with varying structural diversity. Thanks to the proposed classification of structural diversity, differences in results obtained among the tested scenarios become explainable.

Suggested Citation

  • Ferrucci, Francesco & Bock, Stefan & Gendreau, Michel, 2013. "A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods," European Journal of Operational Research, Elsevier, vol. 225(1), pages 130-141.
  • Handle: RePEc:eee:ejores:v:225:y:2013:i:1:p:130-141
    DOI: 10.1016/j.ejor.2012.09.016

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    References listed on IDEAS

    1. Ghiani, Gianpaolo & Guerriero, Francesca & Laporte, Gilbert & Musmanno, Roberto, 2003. "Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies," European Journal of Operational Research, Elsevier, vol. 151(1), pages 1-11, November.
    2. Joris van de Klundert & Laurens Wormer, 2010. "ASAP: The After-Salesman Problem," Manufacturing & Service Operations Management, INFORMS, vol. 12(4), pages 627-641, March.
    3. Bock, Stefan, 2010. "Real-time control of freight forwarder transportation networks by integrating multimodal transport chains," European Journal of Operational Research, Elsevier, vol. 200(3), pages 733-746, February.
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    Cited by:

    1. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    2. Bhusiri, Narath & Qureshi, Ali Gul & Taniguchi, Eiichi, 2014. "The trade-off between fixed vehicle costs and time-dependent arrival penalties in a routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 1-22.
    3. Ferrucci, Francesco & Bock, Stefan, 2015. "A general approach for controlling vehicle en-route diversions in dynamic vehicle routing problems," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 76-87.
    4. repec:eee:transe:v:110:y:2018:i:c:p:110-121 is not listed on IDEAS
    5. Stefan Bock, 2016. "Finding optimal tour schedules on transportation paths under extended time window constraints," Journal of Scheduling, Springer, vol. 19(5), pages 527-546, October.
    6. repec:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0493-4 is not listed on IDEAS


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