The main purpose of this paper was to present the Forestry Routing Optimization Model (FRoM) as a version of the classical Vehicle Routing Problem (VRP). This work approaches for wood logistic problems consisting of simple displacement and multiple displacements of trucks toward the stands. The FRoM encompasses both steps into one single integer mixed linear programming model, considering cranes and trucks schedule, fleet reduction, reduction of overtime, reduction of half-load transportation, and approaching the minimum distance traveled along a fixed planning horizon. Some technique constraints were implemented to provide accurate model function. An executed real problem data was used to compare the outcomes. The objective was to carry and transport 21,881.82 tons of lumber from 10 stands using a total of 48 trucks and 5 cranes in a planning horizon of 6 days, which each day has 20 hours of effective work. The FRoM has performed a fleet reduction of 72.92%, eliminating overtime. It has reduced the half-load trips to the order of 3.17% of all routes. The crane’s analysis allowed catching points of inefficiency due to operational idleness. The FRoM provided savings of 49.12% at all logistic costs. FRoM has shown to be a good option as a route optimizer for forestry logistics.

Air temperature significantly affects the processes involving agricultural and human activities. The knowledge of the temperature of a given location is essential for agricultural planning. It also helps to make decisions regarding human activities. However, it is not always possible to determine this variable. It is necessary to make a precise estimate, using methods that are capable of detecting the existing variations. The aim of this study was to develop models of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) to estimate the mean (Tmean), maximum (Tmax), and minimum (Tmin) monthly air temperatures as a function of geographic coordinates and altitude for different localities in Minas Gerais state, Brazil, with climatic classification Cwa or Cwb. The average monthly data (Tmean, Tmax, and Tmin), over a period of 30 years, were collected from 20 climatological stations. The MLR was able to estimate the Tmax with accuracy. However, the predictive capacity of estimating Tmean and Tmin was low. The algorithms RF and ANN were used to estimate Tmean, Tmax, and Tmin with high accuracy. The best results were obtained using the RF model.

Regression analysis is widely applied in many fields of science to estimate important variables. In general, nonlinear regression is a complex optimization problem and presents intrinsic difficulties in estimating reliable parameters. Nonlinear optimization algorithms commonly require a precise initial estimate to return reasonable estimates. In this work, we introduce a new hybrid algorithm based on the association of a genetic algorithm with the Levenberg–Marquardt method (GALM) to adjust biological nonlinear models without knowledge of initial parameter estimates. The proposed hybrid algorithm was applied to 12 nonlinear models widely used in forest sciences and 12 databases under varying conditions considering classic hypsometric relationships to evaluate the robustness of this new approach. The hybrid method involves two stages; the curve approximation process begins with a genetic algorithm with a modified local search approach. The second stage involves the application of the Levenberg–Marquardt algorithm. The final performance of the hybrid method was evaluated using total fitting for all tested models and databases, confirming the reliability of the proposed algorithm in providing stable parameter estimates. The GA was able to predict the initial parameters, which assisted the LM in converging efficiently. The developed GALM method is effective, and its application is recommended for biological nonlinear analyses.

Background: The principal challenge in forest transportation lies in minimizing the fleet of vehicles and cranes while adhering to operational constraints. Addressing this intricate operational issue yields numerous advantages. This research is dedicated to the development and evaluation of a controlling device designed to enhance timber logistics. A queue simulator is utilized to estimate prospective wait times for the optimized system. Two scenarios are analyzed: one integrating the controlling device and the other excluding it. The study underscores the advantages of vehicle type A, which, due to its higher number of wheelers and fewer cranes compared to vehicle type B, demonstrates greater efficacy in establishing a robust queuing system.

AVALIAÇÃO DA RIQUEZA DE ESPÉCIES ARBÓREAS ATRAVÉS DOS MÉTODOS BOOTSTRAP E REGRESSAO QUADRÁTICA DE PLATÔ

Continuous Forest Inventory (CFI) plays a fundamental role in monitoring and planning commercial planted forests in Brazil. Traditionally, the CFI methodology involves the periodic remeasurement of permanent sample plots, following a sampling rule of approximately 1 plot every 10 hectares (1:10). However, the periodic remeasurement of these permanent plots represents a high cost for companies, driving the search for methods that optimize sampling intensity without compromising the precision and accuracy of estimates. This study proposes a method to optimize the sampling intensity of CFI in eucalyptus plantations in Brazil by combining simulations based on the Central Limit Theorem and an Integer Linear Programming (ILP) model. The developed approach enabled an average reduction of 63% in the number of plots across four distinct regions in Brazil. The accuracy of the estimates was evidenced by an average deviation of -0.6%, -0.7%, and -0.8% for the ages of 4, 5, and 6 years, respectively, demonstrating that the optimized approach maintained the biological pattern of volume estimates compared to the traditional 1:10 sampling intensity scenario. The study reinforces the feasibility of modernizing CFI in Brazil and future improvements, such as incorporating remote sensing data to further enhance the efficiency and adaptability of the methodology.