Personne :
Gaudreault, Jonathan

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Gaudreault
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Jonathan
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Université Laval. Département d'informatique et de génie logiciel
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ncf10571965
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  • Publication
    Restreint
    Agent-based supply-chain planning in the forest products industry
    (Springer, 2008-02-02) Frayret, Jean-Marc; Gaudreault, Jonathan; Rousseau, Alain N.; Harvey, Steve; D'Amours, Sophie
    The new economic challenges and recent trends in globalization have made it very difficult for Canadian forest product companies to improve their financial position without the coordinated involvement of the entire company, including their supply chains (distributed facilities, company offices, industrial customers, and distributors). Such a new level of efficiency involves their distributed facilities and offices spread around the world, and their customers. One consequence of this new reality is that forest products companies are now facing the need to re-engineer their organizational processes and business practices with their partners. To do this they must adopt new technologies to support the coordination of their planning and control efforts in a customer-centered environment. This paper first proposes a generic software architecture for development of an experimentation environment to design and test distributed advanced planning and scheduling systems. This architecture enables combination of agent-based technology and operations research-based tools in order to first take advantage of the ability of agent technology to integrate distributed decision problems, and, second, to take advantage of the ability of operations research to develop and exploit specific normative decision models. Next, this paper describes how this architecture has been configured into an advanced planning and scheduling tool for the lumber industry. Finally, we present how an application of this advanced planning tool is currently being validated and tested in a real manufacturing setting.
  • Publication
    Restreint
    Intégration d'outils APS dans une simulation multi-agent : une application à l'industrie du bois d'œuvre
    (Tec & Doc, 2008-01-01) Frayret, Jean-Marc; Gaudreault, Jonathan; Lemieux, Sébastien; D'Amours, Sophie
    Ces travaux proposent l’intégration d’un système avancé de planification pour l’industrie du sciage de bois résineux dans une simulation multi-agent. L’objectif visé est de permettre la simulation de différences conditions du marché afin de d’évaluer et de comparer l’impact économique de stratégies de pilotage ou de gestion de la demande dans un réseau de création de valeur. Dans cet article, un simulateur à base d’agents est présenté de même qu’un agent simulant le comportement des clients. Un cas d’étude (Virtual Lumber Case) est présenté afin d’illustrer le potentiel d’utilisation du simulateur. Ce cas représente une entreprise canadienne de production de bois d’œuvre, de taille moyenne. Le développement du cas VLC constitue un maillon clé dans le développement de cette recherche; il facilite la création de scénarios de simulation et l’étude de différentes stratégies propres à l’industrie des produits forestiers.
  • Publication
    Restreint
    Chaînes de création de valeur
    (Éditions MultiMondes, 2009-01-01) Martel, Alain; Frayret, Jean-Marc; Gaudreault, Jonathan; D'Amours, Sophie; LeBel, Luc
  • Publication
    Restreint
    Study of the performance of multi-behaviour agents for supply chain planning
    (Elsevier, 2009-06-30) Frayret, Jean-Marc; Gaudreault, Jonathan; Forget, Pascal; D'Amours, Sophie
    In today's industrial context, competitiveness is closely associated to supply chain performance. Coordination between business units is essential to increase this performance, in order to produce and deliver products on time to customers, at a competitive price. While planning systems usually follow a single straightforward production planning process, this paper proposes that partners adapt together their local planning process (i.e. planning behaviours) to the different situations met in the supply chain environment. Because each partner can choose different behaviour and all behaviours will have an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Using agent-based technology, simulation experiments have been undertaken to verify if multi-behaviour planning agents who can change planning behaviours to adapt to their environment can increase supply chain performance. These agents have been implemented in an agent-based planning platform, using a case study illustrating a lumber supply chain. The performance analysis shows that advanced planning systems can take advantage of using multiple planning processes, because of the dynamic context of supply chains.
  • Publication
    Restreint
    Motivations for multi behavior agents in supply chain planning
    (Édition Tec & doc-Lavoisier, 2008-01-01) Frayret, Jean-Marc; Gaudreault, Jonathan; Forget, Pascal; D'Amours, Sophie
    In today’ industrial context competitiveness is closely associated to supply chain performance. Coordiantion between production partners is essential in supply chains to deliver products on time to final clients. As perturbations occur all the time, production centers have to react quickly correct deviances and create new plans, while coordinating changes with partners. Agent-based technology provides a natural approach to model supply chain networks and describe specialized planning agents. To coordinate and optimize their production plan, agents ue specify the heuristic parameters a priori, at the time of their design and prior to their use. The solution proposed here is to give agents the opportunity to change these parameters, modifying their planning behaviors following the environmental conditions met. Using simulation, agents can identify optimal team behaviors for the supply chain in different situations. This paper explains the methodology followed to experiment multi-behavior agents and presents results from an application to the lumber supply chain.
  • 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
    Design of multi-behavior agents for supply chain planning : an application to the lumber industry
    (IntechOpen, 2008-02-01) Frayret, Jean-Marc; Gaudreault, Jonathan; Forget, Pascal; D'Amours, Sophie
  • 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.