RésuméNous considérons des problèmes d'ordonnancement où certains paramètres des tâches sont incertains. Cette incertitude est modélisée au travers d'un ensemble fini de scénarios bien définis. Nous cherchons une solution qui soit acceptable pour l'ensemble des scénarios considérés. Plusieurs critères ont été utilisés dans la littérature pour sélectionner la "meilleure" solution. Nous utilisons ici le critère appelé robustesse absolue. Nous présentons des résultats algorithmiques et de complexité pour quelques problèmes classiques d'ordonnancement sur une machine.
Mots-clefs : Ordonnancement ; Données incertaines ; Scénarios ; Robustesse absolue
AbstractWe consider scheduling environments where some job characteristics are uncertain. This uncertainty is modeled through a finite set of well-defined scenarios. In such a context, we search for a solution that is acceptable for any considered scenario. For this purpose, several criteria can be applied to select among solutions. We use here the so-called absolute robustness criterion. We present algorithmic and computational complexity results for several single machine scheduling problems.
International audienceNon-deterministic lot-sizing models are considered which serve for an explicit determination of lot sizes in an uncertain environment. Taxonomy components for such models are suggested and a bibliography structured according to these components is presented. The taxonomy components are numeric characteristics of a lot-sizing problem, names of uncertain parameters and names of approaches to model the uncertainty. The bibliography covers more than 300 publications since the year 2000
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