Rafiei R.,Laval University |
Rafiei R.,A+ Network |
Nourelfath M.,Laval University |
Nourelfath M.,A+ Network |
And 9 more authors.
International Journal of Production Research | Year: 2014
This article develops an experimental platform to select production planning policy in demand-driven wood remanufacturing industry. This industry is characterised by divergent co-production, alternative processes, a make-to-order philosophy and short order cycle times. Under such complex characteristics, the selection of an efficient production plan is a complex task. Previous work has failed to address all the industrial characteristics encountered in wood remanufacturing mills. After defining key performance indicators (KPIs) to measure the production plan efficiency, our methodology uses a periodic re-planning strategy based on a rolling horizon. Then, mixed-integer programming models are formulated leading to different planning approaches. Finally, the resulting decision framework is experimented to prescribe the best planning policy based on the selected KPI. Each production planning is characterised by its planning approach and factors related to the re-planning interval and the planning horizon length. Simulations are conducted using multiple best subset selections combined with an experimental design approach. Using industrial data from a wood remanufacturing mill in Eastern Canada, results indicate that the manufacturing mill should use a planning approach that minimises cost, while utilising the full system capacity. Results also quantify the benefit of using lower re-planning intervals and higher planning horizons. © 2012 Taylor & Francis.
Santa-Eulalia L.A.,University of Quebec at Montreal |
Santa-Eulalia L.A.,FORAC Research Consortium |
Santa-Eulalia L.A.,Interuniversity Research Center on Enterprise Networks |
Ait-Kadi D.,Laval University |
And 10 more authors.
Production Planning and Control | Year: 2011
This article investigates the robustness of different tactical planning and control policies for a softwood supply chain using an agent-based environment that simulates a distributed advanced planning and scheduling system and its corresponding supply chain operations. Simulations were modelled using a novel agent-based methodology combined with a robust experimental design approach and an industrial data set obtained from two companies in Eastern Canada. Experimental results provide insights about the dynamic relations among factors related to control levels, planning methods and planning horizon lengths. Results indicate that supply chain control levels play a relevant role in defining robust service levels, while the planning horizon and the planning method have lower impact in this context. In addition, from the supply chain inventory level point of view, we verified that the three investigated tactical rules have to be configured together if one desires to maximise their contribution for a robust supply chain system capable of coping with uncertainties from the business environment. When these rules are evaluated individually, it is not possible to make the most of their potential due to interactions between them. The article concludes by proposing an optimum robust configuration of the tactical rules to minimise the impact of supply chain uncertainties. © 2011 Taylor & Francis.
Morin M.,FORAC Research Consortium |
Paradis F.,FORAC Research Consortium |
Rolland A.,FORAC Research Consortium |
Wery J.,FORAC Research Consortium |
And 2 more authors.
Proceedings - Winter Simulation Conference | Year: 2016
We use machine learning to generate metamodels for sawing simulation. Simulation is widely used in the wood industry for decision making. These simulators are particular since their response for a given input is a structured object, i.e., a basket of lumbers. We demonstrate how we use simple machine learning algorithms (e.g., a tree) to obtain a good approximation of the simulator's response. The generated metamodels are guaranteed to output physically realistic baskets (i.e., there exists at least one log that can produce the basket). We also propose to use kernel ridge regression. While having the power to exploit the structure of a basket, it can predict previously unseen baskets. We finally evaluate the impact of possibly predicting unrealistic baskets using ridge regression jointly with a nearest neighbor approach in the output space. All metamodels are evaluated using standard machine learning metrics and novel metrics especially designed for the problem. © 2015 IEEE.