In the sustainable management of Amazonian forests, it is essential to carry out the optimal planning of logging infrastructures to reduce costs and environmental impacts. However, there is a high degree of complexity due to the number of variables involved. Among these infrastructures, wood storage yards are of utmost importance as they directly influence the opening of forest roads and trails. The objective of this research was to evaluate the allocation of wood storage yards through exact solution and metaheuristics in a forest management area. The study area was a native forest under sustainable forest management regime located in the Brazilian Amazon. Three instances were formulated involving 5947 trees and 3172 wood storage yards facilities. We used a binary integer programming model solved by CPLEX and the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP), Tabu Search (TS), Variable Neighborhood Search (VNS) and Simulated Annealing (SA). GAP values increased as a function of instances. Although all metaheuristics obtained significant solutions with shorter processing times, only SA obtained feasible solutions in all executions for all three instances. In general, the metaheuristics were efficient in obtaining feasible solutions faster than CPLEX, which represents the feasibility of the planning of allocation storage large areas, and without significant losses of best-known solution. The SA presented the best performance in the three evaluated instances. Contribution of this study can be highlighted: evaluation of alternative computational methods for planning the allocation of wooden storage yards; evidence was obtained of effectiveness and efficiency of assessed metaheuristics and, the applicability of approximate methods in this problem was evaluated.

在亚马孙森林的可持续管理中，至关重要的是对伐木基础设施进行最佳规划，以降低成本和环境影响。但是，由于涉及的变量数量众多，因此存在高度的复杂性。在这些基础设施中，木材堆场至关重要，因为它们直接影响林道和步道的开放。这项研究的目的是通过森林管理区中的精确解法和超启发式方法评估木材堆场的分配。研究区域是位于巴西亚马逊河地区的，在可持续森林管理制度下的原生森林。制定了三个实例，涉及5947棵树木和3172个木材堆场。GRASP），禁忌搜索（TS），可变邻域搜索（VNS）和模拟退火（SA）。GAP值随实例增加。尽管所有元启发式方法均以较短的处理时间获得了有效的解决方案，但对于所有这三种情况，只有SA在所有执行中均获得了可行的解决方案。通常，元启发法比CPLEX更快地获得可行的解决方案，这表示计划分配存储大面积区域的可行性，而不会损失最著名的解决方案。该SA在三个评估实例中表现出最佳性能。可以强调这项研究的贡献：评估用于规划木制堆场分配的替代计算方法；获得了评估的元启发式方法的有效性和效率的证据，并评估了近似方法在该问题中的适用性。