Abstract Tactical harvest scheduling is related to forest unit selection and spatial sequencing. Mixed Integer Linear Programming (MILP) is traditionally implemented in this type of planning, associating each forest unit with one or more binary and/or integer variables. However, the higher number of binary/integer variables used in a model, the higher complexity and processing time to find an optimal solution. Given the need for improvements in computational performance, heuristic methods may present computational advantages to determine a feasible solution. In this context, the objective of this study was to solve a harvesting plan that aggregates adjacent stands to create blocks through the application of MILP and three different strategies of the Relax-and-Fix (R&F) heuristic. For that purpose, we used a hypothetical 1153-ha forest, subdivided into 100 stands of Pinus taeda L., as a database. The application of R&F, in general, proved to be efficient, especially when the R&F Backward solution was applied as an initial solution of the exact model. This scenario produced the best result of the study: a 28% decrease in computational time and the same spatial sequencing of stands as the MILP model.

中文翻译：

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松弛修复启发式算法在林分聚合中的应用在战术森林调度中

摘要 战术采伐调度与森林单元选择和空间排序有关。混合整数线性规划 (MILP) 传统上在这种类型的规划中实施，将每个森林单元与一个或多个二元和/或整数变量相关联。但是，模型中使用的二进制/整数变量越多，找到最佳解决方案的复杂性和处理时间就越高。鉴于需要改进计算性能，启发式方法可以提供计算优势来确定可行的解决方案。在这种情况下，本研究的目的是解决一个收获计划，该计划通过应用 MILP 和松弛和修复 (R&F) 启发式的三种不同策略来聚合相邻的林分以创建块。为此，我们使用了一个假设的 1153 公顷森林，细分为100个火炬松林分，作为数据库。总的来说，R&F 的应用被证明是有效的，尤其是当 R&F Backward 解决方案用作精确模型的初始解决方案时。这种情况产生了最好的研究结果：计算时间减少了 28%，并且与 MILP 模型相同的林分空间排序。