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&. Mladenovi´cmladenovi´c and . Hansenparragh, 2010) applique cette méthode générale au problème du transport à la demande (DARP) À partir d'une solution initiale, la méthode repose sur diverses heuristiques d'échanges entre véhicules, d'insertions et de décalages de tâches dans leurs plans. Elles servent à construire des solutions « voisines » dont on conserve la plus prometteuse lors d'une recherche locale, avant d'explorer de nouveaux voisinages pour échapper aux minima locaux. La variante stochastique (SVNS) de (Gutjahr et al., 2007) utilise un modèle génératif pour estimer la valeur accumulée par une solution sur un court horizon. Elle a été adaptée au problème de transport à la demande par Introduites par (Bent & Hentenryck, 2004), l'approche par plans multiples (MPA), et son équivalent stochastique l'approche par scénarios multiples (MSA), appliquent la recherche par voisinage à un ensemble de solutions qui évoluent au déclenchement d'événements comme l'arrivée d'une nouvelle requête, ou le départ d'un véhicule, Recherche par voisinage La méthode de recherches par voisinage (VNS) introduite par Une fonction de consensus permet de générer un plan à exécuter en ligne à partir de l'ensemble de solutions. (Schilde et al., 2014) explorent ces différentes variantes) combiné à un algorithme de planification par blocs et mettent en avant l'intérêt de prendre en compte les phénomènes dynamiques et d'anticiper les éléments stochastiques lors de l'optimisation, 1997.

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