Machine learning-based models of sawmills for better wood allocation planning

Michael Morin, Jonathan Gaudreault, Edith Brotherton, Frédérik Paradis, Amélie Rolland, Jean Wery and François Laviolette

International Journal of Production Economics, 2020, vol. 222, issue C

Abstract: 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.

Keywords: Wood allocation planning; Sawing simulation; Machine learning application (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:222:y:2020:i:c:s0925527319303287

DOI: 10.1016/j.ijpe.2019.09.029

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