New strategies for stochastic resource-constrained ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
New strategies for stochastic resource-constrained project scheduling
Auteur(s) :
Rostami, Salim [Auteur]
Creemers, Stefan [Auteur]
Lille économie management - UMR 9221 [LEM]
Leus, Roel [Auteur]
Catholic University of Leuven = Katholieke Universiteit Leuven [KU Leuven]
Creemers, Stefan [Auteur]
Lille économie management - UMR 9221 [LEM]
Leus, Roel [Auteur]
Catholic University of Leuven = Katholieke Universiteit Leuven [KU Leuven]
Titre de la revue :
Journal of Scheduling
Pagination :
349–365
Éditeur :
Springer Verlag
Date de publication :
2017-01-12
ISSN :
1094-6136
Discipline(s) HAL :
Sciences de l'Homme et Société/Gestion et management
Résumé en anglais : [en]
We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a ...
Lire la suite >We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a priori decisions in a preprocessing phase, while the remaining scheduling decisions are made online. A two-phase local search algorithm is proposed to optimize within the class. Our computational results show that the algorithm has been efficiently tuned toward finding high-quality solutions and that it outperforms all existing algorithms for large instances. The results also indicate that the optimality gap even within the larger class of elementary policies is very small.Lire moins >
Lire la suite >We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a priori decisions in a preprocessing phase, while the remaining scheduling decisions are made online. A two-phase local search algorithm is proposed to optimize within the class. Our computational results show that the algorithm has been efficiently tuned toward finding high-quality solutions and that it outperforms all existing algorithms for large instances. The results also indicate that the optimality gap even within the larger class of elementary policies is very small.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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