Enhancements of NSGA II and Its Application to the Vehicle Routing Problem with Route Balancing

  • Nicolas Jozefowiez
  • Frédéric Semet
  • El-Ghazali Talbi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)


In this paper, we address a bi-objective vehicle routing problem in which the total length of routes is minimized as well as the balance of routes, i.e. the difference between the maximal route length and the minimal route length. For this problem, we propose an implementation of the standard multi-objective evolutionary algorithm NSGA II. To improve its efficiency, two mechanisms have been added. First, a parallelization of NSGA II by means of an island model is proposed. Second, an elitist diversification mechanism is adapted to be used with NSGA II. Our method is tested on standard benchmarks for the vehicle routing problem. The contribution of the introduced mechanisms is evaluated by different performance metrics. All the experimentations indicate a strict improvement of the generated Pareto set.


Pareto Optimal Solution Vehicle Route Problem Island Model Route Length Capacitate 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nicolas Jozefowiez
    • 1
  • Frédéric Semet
    • 2
  • El-Ghazali Talbi
    • 1
  1. 1.Laboratoire d’Informatique Fondamentale de LilleUniversité des Sciences et Technologies de LilleVilleneuve d’AscqFrance
  2. 2.Laboratoire d’AutomatiqueUniversité de Valenciennes et du Hainaut-CambrésisValenciennesFrance

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