International Journal of Industrial Engineering and Management

Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut ero labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco.

GUIDE FOR AUTHORS SUBMIT MANUSCRIPT
Vol. 6 No. 2 (2015)
Original Research Article

A Metaheuristic ACO to Solve the Multi-Depot Vehicle Routing Problem with Backhauls

Jhon Jairo Santa Chávez
Universidad Libre Seccional Pereira
Bio
Mauricio Granada Echeverri
Universidad Tecnológica de Pereira
Bio
John Willmer Escobar
Pontificia Universidad Javeriana Cali
Bio
César Augusto Peñuela Meneses
Universidad Libre Seccional Pereira
Bio

Published 2015-06-30

abstract views: 10 // FULL TEXT ARTICLE (PDF): 0


Keywords

  • Ant Colony,
  • Backhauls,
  • Combinatorial Optimization,
  • Computational Simulation,
  • Multi-Depot Vehicle Routing Problem

How to Cite

Santa Chávez, J. J., Echeverri, M. G., Escobar, J. W., & Peñuela Meneses, C. A. (2015). A Metaheuristic ACO to Solve the Multi-Depot Vehicle Routing Problem with Backhauls. International Journal of Industrial Engineering and Management, 6(2), 49–58. https://doi.org/10.24867/IJIEM-2015-2-107

Abstract

In this paper, a metaheuristic algorithm based on the ant colony optimization is presented to solve the multi-depot vehicle routing problem with delivery and collection of package. Each performed route consists of one sub-route in which only the delivery task is done, in addition to one sub-route in which only the collection process is performed. The proposed algorithm tries to find the best order to visit the customers at each performed route. In addition, the proposed approach determines the best connection between the sub-routes of delivery and collection, in order to obtain a global solution with the minimum travelling cost. The efficiency of the proposed algorithm has been evaluated by considering a set of instances adapted from the literature. The computational results have been compared with a greedy heuristic algorithm based on the nearest neighborhood approach. Finally, conclusions and suggestions for future works are presented.

 

Article history: Received (11.05.2015); Revised (17.06.2015); Accepted (03.07.2015)  

PlumX Metrics

Dimensions Citation Metrics