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

Tactical 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...

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Bibliographic Details
Published in:Forests 2023-09, Vol.14 (9), p.1782
Main Authors: Munis, Rafaele Almeida, Almeida, Rodrigo Oliveira, Camargo, Diego Aparecido, da Silva, Richardson Barbosa Gomes, Wojciechowski, Jaime, Simões, Danilo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Datasets
Decision making
Environment
Eucalyptus
Forest harvesting
Forest management
Forests
Harvest
Log transportation
Machine learning
Modelling
Performance measurement
Pinus
Planning
Predictive analytics
Productivity
Resource allocation
Supervised learning
Timber
Transportation
Variables
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Summary:Tactical 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.
ISSN:1999-4907
1999-4907
DOI:10.3390/f14091782