An evolutionary algorithm for the vehicle ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
An evolutionary algorithm for the vehicle routing problem with route balancing
Auteur(s) :
Jozefowiez, Nicolas [Auteur]
LAAS-MOGISA
Semet, Frédéric [Auteur]
LAGIS-OSL
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
LAAS-MOGISA
Semet, Frédéric [Auteur]
LAGIS-OSL
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Titre de la revue :
European Journal of Operational Research
Pagination :
761-769
Éditeur :
Elsevier
Date de publication :
2009-06-16
ISSN :
0377-2217
Mot(s)-clé(s) en anglais :
Routing
Multi-objective optimization
Genetic algorithms
Parallel algorithms
Multi-objective optimization
Genetic algorithms
Parallel algorithms
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
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 ...
Lire la suite >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. We propose a meta-heuristic method based on an evolutionary algorithm involving classical multi-objective operators. To improve its efficiency, two mechanisms, which favor the diversification of the search, have been added. First, an elitist diversification mechanism is used in cooperation with classical diversification methodologies. Second, a parallel model designed to take into account the elitist diversification is proposed. 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.Lire moins >
Lire la suite >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. We propose a meta-heuristic method based on an evolutionary algorithm involving classical multi-objective operators. To improve its efficiency, two mechanisms, which favor the diversification of the search, have been added. First, an elitist diversification mechanism is used in cooperation with classical diversification methodologies. Second, a parallel model designed to take into account the elitist diversification is proposed. 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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
Fichiers
- fulltext.pdf
- Accès libre
- Accéder au document