A comparative study between dynamic adapted ...
Type de document :
Article dans une revue scientifique: Article original
Titre :
A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests
Auteur(s) :
Khouadjia, Mostepha Redouane [Auteur]
Machine Learning and Optimisation [TAO]
Sarasola, Briseida [Auteur]
Departamento Lenguajes y Ciencias de la Computación [Malaga] [LCC]
Alba, Enrique [Auteur]
Departamento Lenguajes y Ciencias de la Computación [Malaga] [LCC]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Machine Learning and Optimisation [TAO]
Sarasola, Briseida [Auteur]
Departamento Lenguajes y Ciencias de la Computación [Malaga] [LCC]
Alba, Enrique [Auteur]
Departamento Lenguajes y Ciencias de la Computación [Malaga] [LCC]
Jourdan, Laetitia [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Titre de la revue :
Applied Soft Computing
Pagination :
1426-1439
Éditeur :
Elsevier
Date de publication :
2012-04
ISSN :
1568-4946
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems ...
Lire la suite >Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.Lire moins >
Lire la suite >Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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