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Timber transportation is a hard task for any forest company. There are several efforts to reduce and control these costs, considering efficient equipment and optimization frameworks. Due to the pattern of combinatorial problems, the deterministic methods have a high computational effort of processing time, which generally makes their use unfeasible. An alternative procedure is applying approximation algorithms, which have efficient searches for finding feasible solutions. The present study evaluated algorithms for solving the forest transport sequencing problem. The Simulated instances were designed to highlight three instances for 10, 20, and 30 stands with a realistic pattern. According to the operational set, four solving strategies were proposed, considering three algorithms (Simulated Annealing, Greedy, and Greedy-Simulated Annealing). The computational code, processing, and analysis of the data were performed using RStudio software. The results show feasible solutions from all tested algorithms, highlighting the hybrid Greedy-Simulated Annealing algorithm. With proven normality and homogeneity of variance, the algorithms were tested with the Tukey test at a level of 5%. The blocks, stand random selection, and multiple flow strategies produced the best results of the tested instances. As a conclusion, the proposed algorithms are efficient for solving the forest transportation problem and may be helpful at the operational planning level. [ABSTRACT FROM AUTHOR]

Forest ecosystems are important in the carbon storage process. Thus, the objective was to investigate the effectiveness of the Simulated Annealing metaheuristic analysis for selecting variables to maximize the accuracy of the aboveground carbon prediction at the tree level. We used data from uneven-aged forests located in the Rio Grande Basin - Minas Gerais, Brazil, where 227 trees had their carbon stock measured. The classic Spurr linear model, stepwise linear regression and pan-tropical coverage, Random Forest (RF), and the hybrid SARF method (Simulated Annealing and Random Forest) were used to estimate the carbon stock from the selection of variables for the different compartments of the tree (total, stem, branch, and leaf). The SARF consisted of the metaheuristic to select the variables to be used in the RF. These methods were evaluated by the root mean square error (RMSE), coefficient of determination (R²), and residual graph. As a result, the pan-tropical equation demonstrated superior performance than the Spurr model due to its greater homogeneity of residues. The stepwise technique reduced the number of variables and the error of the estimates, mainly for the validation set. SARF showed better adjustments than RF, as it reduced in on average 99.2% of the number of variables and 9% of the error of estimates considering all compartments. In general, variables such as volume, basic wood density, canopy projection area, diameter at 0%, diameter at breast height, height, and latitude contributed strongly to the carbon independent of the tree compartment. Among the methods, SARF is an alternative to the traditional method, as it can extract accurate information from a large data set. [ABSTRACT FROM AUTHOR]