We present a learning heuristic using dynamic programming (DP) formulations to address both spatial and temporal detail in multiobjective forest management planning. The problem is decomposed into smaller problems to avoid the curse of dimensionality associated with DP. The heuristic learns from multiple decomposed problem formulations to identify stands assigned the same management option regardless of formulation. Consistently managed stands are recognized, and the problem is eventually distilled to the most difficult to solve portions of the forest. The heuristic is demonstrated on a forest management problem with short‐lived core area wildlife habitat, where temporal detail is important. The problem is large due to the number of stand‐level management timing options and associated interactions with the management timings of nearby stands. Results show solution improvement along with substantial time savings over previously used heuristics. The learning heuristic enables analysis of large problems that emerge with high levels of spatial and temporal detail.

中文翻译：

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在森林规划中整合时空细节的学习启发法

我们提出了一种使用动态规划（DP）公式来解决多目标森林管理规划中的时空细节的学习启发式方法。该问题被分解为较小的问题，以避免与DP相关的维数的诅咒。启发式方法从多个分解的问题公式中学习，以识别分配了相同管理选项的机架，而不管公式如何。人们认识到管理一致的林分，最终将问题归结为最难解决的部分森林。启发式方法是针对森林管理问题以及核心区域野生生物栖息地较短的问题而提出的，在该问题中时间细节很重要。由于展位级管理时间选项的数量以及与附近展位管理时间的关联交互作用，该问题很大。结果表明，与以前使用的启发式方法相比，解决方案得到了改进，并节省了大量时间。通过学习启发式方法，可以分析随时间和空间的高度细节而出现的大问题。