A Multiobjective Memetic Approach to ...
Type de document :
Communication dans un congrès avec actes
Titre :
A Multiobjective Memetic Approach to Job-Shop Scheduling under Uncertainty
Auteur(s) :
Tran, Thanh-Do [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
González-Rodríguez, Inés [Auteur]
Department of Mathematics, Statistics and Computing
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
González-Rodríguez, Inés [Auteur]
Department of Mathematics, Statistics and Computing
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Titre de la manifestation scientifique :
The 5th International Conference on Metaheuristics and Nature Inspired Computing (META'14)
Ville :
Marrakech
Pays :
Maroc
Date de début de la manifestation scientifique :
2014-10-27
Date de publication :
2014-10-27
Mot(s)-clé(s) en anglais :
job-shop scheduling
multiobjective memetic algorithm
uncertainty
robustness analysis
multiobjective memetic algorithm
uncertainty
robustness analysis
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
In recent years, much work has been expended on addressing job-shop scheduling problems (JSP) with uncertain information. There are two primary approaches to uncertainty handling, i.e. using probability theory and possibility ...
Lire la suite >In recent years, much work has been expended on addressing job-shop scheduling problems (JSP) with uncertain information. There are two primary approaches to uncertainty handling, i.e. using probability theory and possibility theory. In this work, we use the possibilistic approach to deal with JSP where uncertain processing times are modeled by triangular fuzzy numbers (TFNs). Algorithmically, this paper examines the incorporation of a local search into a multiobjective genetic approach. The incorporation results in a simple multiobjective memetic algorithm that is based on the NSGA-II and the N2 neighborhood structure for individual improvement in the Lamarckian learning procedure. An extensive experiment was conducted to confirm the superiority of the algorithm compared to both the single-objective memetic and multiobjective genetic methods.Lire moins >
Lire la suite >In recent years, much work has been expended on addressing job-shop scheduling problems (JSP) with uncertain information. There are two primary approaches to uncertainty handling, i.e. using probability theory and possibility theory. In this work, we use the possibilistic approach to deal with JSP where uncertain processing times are modeled by triangular fuzzy numbers (TFNs). Algorithmically, this paper examines the incorporation of a local search into a multiobjective genetic approach. The incorporation results in a simple multiobjective memetic algorithm that is based on the NSGA-II and the N2 neighborhood structure for individual improvement in the Lamarckian learning procedure. An extensive experiment was conducted to confirm the superiority of the algorithm compared to both the single-objective memetic and multiobjective genetic methods.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.inria.fr/hal-01110315/document
- Accès libre
- Accéder au document
- https://hal.inria.fr/hal-01110315/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- meta14_tran.pdf
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- meta14_tran.pdf
- Accès libre
- Accéder au document