Problema de roteamento veicular aplicado ao inventário florestal

Abstract

The logistics is essential for the effective functioning of the supply chain, encompassing activities such as warehousing and distribution, transportation, purchasing, planning, and quality management. In the forestry sector, logistics is particularly critical during the harvest and transportation phases; however, it is also vital for preliminary stages like planting and forest management. Forest inventory plays a crucial role by providing data for the planning of plantings and estimating biomass production. Efficient management of inventory routes is indispensable for minimizing operational costs and improving the effectiveness of forestry operations, ensuring that activities are carried out in an optimized and economical manner. The primary objective of this study is to implement the Simulated Annealing (SA) algorithm to enhance the planning of forest inventory routes. The research aims to evaluate how this technique can reduce the distance traveled and the time required for teams to move between locations, contributing to a more efficient management of forestry resources and operations. The study was conducted in plots of GELF Siderurgia S.A., located in the Itacambira region. The SA algorithm was applied to optimize the planning of forest inventory routes, with the goal of improving logistical efficiency. Field data were meticulously collected and analyzed to validate the proposed model and assess its applicability and effectiveness in the specific context of the research. The application of SA resulted in a significant reduction of 8% in both the distance traveled and the time required for teams to move. These results demonstrate the effectiveness of the SA method compared to other optimization techniques described in the literature, highlighting its ability to provide substantial improvements in logistical operations. The findings of this study confirm that the SA algorithm is an effective tool for logistical optimization in the forestry sector. The performance of SA was comparable to and, in some cases, superior to methods such as Particle Swarm Optimization (PSO), Tabu Search and Bacterial Foraging Optimization (BCO). This suggests that SA may be a preferred choice for similar routing and logistical planning problems. The use of the SA algorithm in forest inventory routing has proven to be highly effective, resulting in a significant reduction in both distance and travel time. The research contributes to the advancement of planning and optimization techniques in forest inventory management, offering a robust approach that can be applied in various contexts and regions. These results promote more efficient management of forest resources and establish a solid foundation for future investigations and practical applications of the technique.