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In this paper, we introduce a Yet ImprovEd Limited Discrepancy Search (YIELDS), a complete algorithm for solving Constraint Satisfaction Problems. As indicated in its name, YIELDS is an improved version of Limited Discrepancy Search (LDS). It integrates constraint propagation and variable order learning. The learning scheme, which is the main contribution(More)
In this paper, we propose mechanisms to improve instantiation heuristics by incorporating weighted factors on variables. The proposed weight-based heuristics are evaluated on several tree search methods such as chronological backtracking and discrepancy-based search for both constraint satisfaction and optimization problems. Experiments are carried out on(More)
R ´ ESUMÉ : Dans le cadre de la résolution desprobì emes d'ordonnancement et de la résolution par des méthodes arborescentes, nous nous intéressons dans ce travaiì a la résolution desprobì emes d'ordonnancement avec contraintes de délais par les méthodesà base de divergence. Nous focalisons ainsi notré etude sur les probì emes de type flowshop et jobshops(More)
This paper addresses the jobshop and the flowshop scheduling problems with minimum and maximum time lags. To solve this kind of problems, we propose adaptations of Climbing Discrepancy Search (CDS). We study various parameter settings. Computational experiments are provided to evaluate the propositions.
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