Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal University of Paraná-
Autor(es): dc.creatorMunis, Rafaele Almeida-
Autor(es): dc.creatorAlmeida, Rodrigo Oliveira-
Autor(es): dc.creatorCamargo, Diego Aparecido-
Autor(es): dc.creatorda Silva, Richardson Barbosa Gomes-
Autor(es): dc.creatorWojciechowski, Jaime-
Autor(es): dc.creatorSimões, Danilo-
Data de aceite: dc.date.accessioned2025-08-21T16:23:06Z-
Data de disponibilização: dc.date.available2025-08-21T16:23:06Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/f14091782-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308308-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308308-
Descrição: dc.descriptionTactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning.-
Descrição: dc.descriptionDepartment of Forest Science Soils and Environment School of Agriculture São Paulo State University (UNESP)-
Descrição: dc.descriptionInformatics Department Federal University of Paraná-
Descrição: dc.descriptionDepartment of Forest Science Soils and Environment School of Agriculture São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationForests-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcut-to-length harvesting-
Palavras-chave: dc.subjectforest operations-
Palavras-chave: dc.subjectforest productivity-
Palavras-chave: dc.subjectintelligent forest management-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectpredictive analytics-
Título: dc.titleTactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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