, Générer en parallèle les solutions initiales P q

, Définir les blocs et la grille comme suit : dim3 dimBlock(t, 1)

, Exécuter en parallèle le kernel GA avec les solutions initiales P q

, Sélectionner la solution avec le coût minimal parmi les M solutions

, résultats par rapportà ceux obtenus par Ernst et Krishnamoorthy [58] pour le CSAHLP et donne les hubs de la solution optimale / meilleure, pour les instances AP jusqu'à 200 noeuds (les grandes instances publiées pour ce problèmeà notre connaissance). Récemment Corberan et al. [44] ont généré des instances jusqu'à 250 noeuds, mais pour le CSAHLP avec des capacités identiques pour le noeud et des capacités de liaison pour les arcs. Le temps d'exécution sur GPU requis

, Le processus est répété N 2 fois, comme il peut s'arrêter s'il n'y a pas d'amélioration de la solution robuste finale pendant 20 itérations. Le pseudo-code CUDA pour cette implémentation est le suivant : 1. Copier la matrice de distance, Chapitre 5. Problème de localisation des hubs avec des données incertaines créer les nouvelles populations

, Générer la solution initiale

, Définir les blocs et la grille comme suit : dim3 dimBlock(t, 1)

G. Exécuter-en-parallèle-le-kernel and . Dimgrid, , p.dimBlock >>> (

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