An evolutionary algorithm for the vehicle ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
An evolutionary algorithm for the vehicle routing problem with route balancing
Author(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]
Journal title :
European Journal of Operational Research
Pages :
761-769
Publisher :
Elsevier
Publication date :
2009-06-16
ISSN :
0377-2217
English keyword(s) :
Routing
Multi-objective optimization
Genetic algorithms
Parallel algorithms
Multi-objective optimization
Genetic algorithms
Parallel algorithms
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
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