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DOI: 10.14569/IJACSA.2018.090644
PDF

Ant Colony System for Dynamic Vehicle Routing Problem with Overtime

Author 1: Khaoula OUADDI
Author 2: Youssef BENADADA
Author 3: Fatima-Zahra MHADA

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 6, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Traditionally, in a VRP the vehicles return to depot before the end of the working time. However, in reality several constraints can occur and prevent the vehicles from being at the depot on time. In the dynamic case, we are supposed to answer the requests the same day of their arrival. Nevertheless, it is not always easy to find a solution, which ensures the service while respecting the normal working time. Therefore, allowing the vehicle to use additional time to complete their service may be very useful especially if we have a large demand with a limited number of vehicles. In this context, this article proposes a mathematical modeling with an Ant Colony System (ACS) based approach to solve the dynamic vehicle routing problem (DVRP) multi-tours with overtime. To test the algorithm, we propose new data sets inspired from literature benchmarks. The competitiveness of the algorithm is proved on the classical DVRP.

Keywords: Dynamic vehicle routing problem (DVRP); multi-tours; mathematical modeling; hybrid; Ant Colony System (ACS); overtime

Khaoula OUADDI, Youssef BENADADA and Fatima-Zahra MHADA, “Ant Colony System for Dynamic Vehicle Routing Problem with Overtime” International Journal of Advanced Computer Science and Applications(IJACSA), 9(6), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090644

@article{OUADDI2018,
title = {Ant Colony System for Dynamic Vehicle Routing Problem with Overtime},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090644},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090644},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {6},
author = {Khaoula OUADDI and Youssef BENADADA and Fatima-Zahra MHADA}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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