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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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  • 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
    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
    Accès libre
    Machine learning-based models of sawmills for better wood allocation planning
    (Elsevier, 2020-03-11) Morin, Michael; Gaudreault, Jonathan; Brotherton, Edith; Paradis, Frédérik; Rolland, Amélie; Wéry, Jean; Laviolette, François
    The forest-products supply chain gives rise to a variety of interconnected problems. Addressing these problems is challenging, but could be simplified by rigorous data analysis through a machine learning approach. A large amount of data links these problems at various hierarchical levels (e.g., strategic, tactical, operational, online) which complicates the data computation phase required to model and solve industrial problem instances. In this study, we propose to use machine learning to generate models of the sawmills (converting logs into lumber) to simplify the data computation phase for solving optimization problems. Specifically, we show how to use these models to provide a recommendation for the allocation of cutblocks to sawmills for a wood allocation planning problem without needing extensive sawing simulations. Our experimental results on an industrial problem instance demonstrate that the generated models can be used to provide high-quality recommendations (sending the right wood to the right mill). Machine learning models of the sawmill transformation process from logs to lumber allows a better allocation exploiting the strengths of the mills to process the logs in our industrial case.
  • Publication
    Restreint
    Combined planning and scheduling in a divergent production system with co-production : a case study in the lumber industry
    (Elsevier, 2010-11-20) Frayret, Jean-Marc; Gaudreault, Jonathan; Rousseau, Alain N.; D'Amours, Sophie
    Many research initiatives carried out in production management consider process planning and operations scheduling as two separate and sequential functions. However, in certain contexts, the two functions must be better integrated. This is the case in divergent production systems with co-production (i.e. production of different products at the same time from a single product input) when alternative production processes are available. This paper studies such a context and focuses on the case of drying and finishing operations in a softwood lumber facility. The situation is addressed using a single model that simultaneously performs process planning and scheduling. We evaluate two alternative formulations. The first one is based on mixed integer programming (MIP) and the second on constraint programming (CP). We also propose a search procedure to improve the performance of the CP approach. Both approaches are compared with respect to their capacity to generate good solutions in short computation time.
  • Publication
    Accès libre
    Kriging analysis of an integrated demand management process in softwood industry
    (Elsevier, 2017-10-18) Gaudreault, Jonathan; Carle, Marc-André; Ben Ali, Maha; D'Amours, Sophie
    Objective: This paper aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at time. Yet, we can get additional insights by employing design of experiments (DOE). Methodology: For making promises, we compare a First-Come First-Served approach to an approach using nested booking limits and giving advantage to profitable customers and attractive periods. Considering various sequences of order arrival, we generate Kriging metamodels that best describe the nonlinear relationships between the simulation responses and system factors for Canadian softwood lumber firms. We employ a Latin hypercube design to take into account different environmental scenarios. Results: Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling less-priority orders and if we use nested booking limits concept.
  • Publication
    Accès libre
    Integrating revenue management and sales and operations planning in a Make-To-Stock environment : softwood lumber case study
    (INFOR Journal, 2019-02-26) Gaudreault, Jonathan; Carle, Marc-André; Ben Ali, Maha; D'Amours, Sophie
    Most research regarding revenue management in manufacturing has considered only a short-term planning horizon, assuming supply and production data exogenously given. Motivated by the case of the Canadian softwood lumber industry, this paper offers additionally a medium-term visibility for firms with limited capacity and faced with seasonal markets. We propose a demand management process for Make-To-Stock environments, integrating sales and operations planning (S&OP) and order promising based on revenue management concepts. Given heterogeneous customers, divergent product structure and multiple sourcing locations in a multi-period context, we first define a multi-level decision framework in order to support medium-term, short-term and real-time sales decisions in a way to maximize profits and to enhance the service level offered to high-priority customers. We further propose a mathematical formulation integrating an S&OP network model in the Canadian softwood lumber industry and an order promising model using nested booking limits. This new formulation allows reviewing previous order promising decisions while respecting sales commitments. A rolling horizon simulation is used to evaluate the performance of the proposed process in various demand scenarios and provides evidence that better performances can be achieved compared to common demand management practices by integrating S&OP and revenue management concepts.
  • Publication
    Accès libre
    Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling
    (Elsevier, 2018-01-09) Gaudreault, Jonathan; Carle, Marc-André; Ben Ali, Maha; D'Amours, Sophie
    Objective This research aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies with limited capacity. Contribution Typical researches about demand management processes analyze few system specifications or vary few potential factors one at a time. Yet, additional insights can be obtained by employing a space-filling design and Kriging metamodeling for analysis. Methodology We compare two configurations of the integrated demand management process. While the First-Come First-Served concept is used at the order promising level for the first configuration, the second configuration uses nested booking limits and gives advantage to profitable customers and attractive periods. Considering various order arrival sequences, we generate Kriging metamodels that best describe the nonlinear relationships between four environmental factors (demand intensity, demand forecast error, customer heterogeneity and coefficient of variation) and three performance measures (yearly profit margin, yearly sales and high-priority fill rate) for Canadian softwood lumber firms. Since our simulation experiments are time-consuming, we employ a Latin hypercube design to efficiently take into account different market situations. Results Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling low-priority orders and if we use nested booking limits concept.
  • Publication
    Accès libre
    Toward digital twins for sawmill production planning and control : benefits, opportunities and challenges
    (Taylor and Francis, 2022-05-12) Morin, Michael; Chabanet, Sylvain; Gaudreault, Jonathan; El-Haouzi, Hind Bril; Thomas, Philippe; Chabanet, Sylvain; El-Haouzi, Hind Bril
    Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to severalfactors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied.
  • Publication
    Restreint
    Coordination mechanism design in supply chains using multi-behaviour agents
    (Olney Inderscience Enterprises, 2010-07-19) Frayret, Jean-Marc; Gaudreault, Jonathan; Forget, Pascal; D'Amours, Sophie
    In today's industrial context, competitiveness is closely associated with supply chain performance. The ongoing development of integrated supply chains has increased the importance of supply chain management. This paper focuses on the design of coordination mechanisms that determine how partners interact to coordinate their production planning. We present a simulation tool to aid in the design of coordination mechanisms using multi-behaviour agents in an agent-based planning system where agents act as partners. The implementation of this solution is realised through the FORAC agent-based planning platform, which is dedicated to supply chain production planning and simulation for the lumber industry.