Developing reliable and fast simulated annealing for stand-level forest harvesting schedule with virtual dimensionality reduction
Developing reliable and fast simulated annealing for stand-level forest harvesting schedule with virtual dimensionality reduction
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DOI：
10.1016/j.compag.2021.106494
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Kai Moriguchi
中科院分区：
文献类型：
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作者：
Kai Moriguchi
确定林分（林分水平）的最佳采伐（间伐和皆伐）时间表对森林管理者和研究者来说非常重要。然而，提供高度可靠的最佳收获时间表仍然需要相当多的计算时间。本研究旨在发展一种以模拟退火为基础的方法，以提供一个高度可靠的最佳时间表，在短时间内收获。在实践中，循环中的收获事件的最佳数量可能并不大。因此，1）将采伐年龄和间伐强度作为控制变量，2）迭代搜索最优间伐事件数，实际上降低了调度问题的维数。该方法是通过与固定收获年龄模型的可靠方法进行比较而开发的。提出了三种候选邻域方法，并进行了比较。其中一种邻域方法的鲁棒性低于其他两种方法。我们进一步开发了一种方法，以优化种植密度，同时收获时间表。引入种植密度作为控制变量，通过减少几种情况下的最佳收获次数来减少计算时间。在大多数情况下，所开发的方法提供了比以前的方法少得多的时间的最佳时间表。
Identifying optimal harvesting (thinning and clearcutting) schedule for a forest stand (stand-level) is of great importance to forest managers and researchers. However, providing highly reliable optimal harvesting schedules still requires considerable computation time. This study aimed to develop a simulated-annealing-based method to provide a highly reliable optimal schedule for harvesting in a short time. In practice, the optimal number of harvesting events in a rotation may not be large. Therefore, 1) treating harvesting ages as well as the thinning intensity as control variables, and 2) searching the optimal number of thinning events iteratively, the dimension of the scheduling problem was virtually reduced. The method was developed through a comparison with a reliable method for a model with fixed harvesting ages. Three candidate neighborhood methods were developed and compared with each other. One neighborhood method was less robust than the other two methods. We further developed a method to optimize the planting density simultaneously with the harvesting schedule. Introduction of planting density as a control variable reduced computation time by decreasing the optimal number of harvesting for several cases. The developed method provided optimal schedules with much less time than the previous method for most cases.