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Gaudreault, Jonathan

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Université Laval. Département d'informatique et de génie logiciel
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  • Publication
    Accès libre
    Distributed operations planning in the softwood lumber supply chain : models and coordination
    (Centre interuniversitaire de recherche sur les réseaux d'entreprises, la logistique et le transport, 2009-02-01) Frayret, Jean-Marc; Gaudreault, Jonathan; Rousseau, Alain N.; Forget, Pascal; D'Amours, Sophie
    Agent-based technology provides a natural approach to model supply chain networks. Each production unit, represented by an agent, is responsible for planning its operations and uses communication to coordinate with the others. In this paper, we study a softwood lumber supply chain made of three planning units (sawing unit, drying unit and finishing unit). We define the problems and propose agent-specific mathematical models to plan and schedule operations. Then, in order to coordinate these plans between the three agents, we propose different coordination mechanisms. Using these developments, we show how an agent-based simulation tool can be used to integrate planning models and evaluate different coordination mechanisms.
  • Publication
    Accès libre
    ADS : an adaptive search strategy for efficient distributed decision making
    (Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport, 2008-11-01) Frayret, Jean-Marc; Pesant, Gilles; Gaudreault, Jonathan; D'Amours, Sophie
    This paper concerns distributed decision-making in hierarchical settings. For this class of problems, the coordination space can be naturally modeled as a tree. A collective of agents can thus perform a distributed tree search in order to coordinate. Previous results have shown that search strategies based on discrepancies (e.g. LDS) can be adapted to a distributed context. They are more effective than chronological backtracking in such setting. In this paper we introduce ADS, an adaptive backtracking strategy based on the analysis of discrepancies. It enables the agents to collectively and dynamically learn which areas of the tree are most promising in order to visit them first. We evaluated the method using a real coordination problem in an industrial supply chain. This makes it possible for the team of agents to obtain high-quality solutions much more quickly than with previous methods.