Research Highlights: (1) Optimizing mid-rotation thinning increased modeled land expectation values by as much as 5.1–10.1% over a representative reference prescription on plots planted at 2.7 and 3.7 m square spacings. (2) Eight heuristics, five of which were newly applied to selecting individual trees for thinning, produced thinning prescriptions of near identical quality. (3) Based on heuristic sampling properties, we introduced a variant of the hero heuristic with a 5.3–20% greater computational efficiency. Background and Objectives: Thinning, which is arguably the most subjective human intervention in the life of a stand, is commonly executed with limited decision support in tree selection. This study evaluated heuristics’ ability to support tree selection in a factorial experiment that considered the thinning method, tree density, thinning age, and rotation length. Materials and Methods: The Organon growth model was used for the financial optimization of even age Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) harvest rotations consisting of a single thinning followed by clearcutting on a high-productivity site. We evaluated two versions of the hero heuristic, four Monte Carlo heuristics (simulated annealing, record-to-record travel, threshold accepting, and great deluge), a genetic algorithm, and tabu search for their efficiency in maximizing land expectation value. Results: With 50–75 year rotations and a 4% discount rate, heuristic tree selection always increased land expectation values over other thinning methods. The two hero heuristics were the most computationally efficient methods. The four Monte Carlo heuristics required 2.8–3.4 times more computation than hero. The genetic algorithm and the tabu search required 4.2–8.4 and 21–52 times, respectively, more computation than hero. Conclusions: The accuracy of the resulting thinning prescriptions was limited by the quality of stand measurement, and the accuracy of the growth and yield models was linked to the heuristics rather than to the choice of heuristic. However, heuristic performance may be sensitive to the chosen models.

中文翻译：

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稀疏花旗松的启发式优化

研究要点：（1）优化中转稀疏比在2.7和3.7 m平方间距上种植的田地上的典型土地预期值高出典型参考处方值5.1-10.1％之多。（2）八种启发式方法，其中五种是新近用于选择稀疏树木的方法，产生了几乎相同质量的稀疏处方。（3）基于启发式采样属性，我们引入了英雄启发式的一种变体，其计算效率提高了5.3–20％。背景与目标：间伐可以说是林分生命中最主观的人工干预，通常在树木选择的决策支持有限的情况下进行。这项研究在一项因子分析实验中评估了启发式方法支持树选择的能力，该实验考虑了细化方法，树密度，变薄的年龄和旋转长度。资料和方法：将Organon增长模型用于平均年龄Douglas-fir（Pseudotsuga menziesii（Mirb。）Franco）轮作的收获包括一次间伐，然后在高产地进行整地。我们评估了英雄启发法的两种版本，四种蒙特卡洛启发法（模拟退火，记录到记录的行程，阈值接受和大量洪水），遗传算法和禁忌搜索，以求出它们在最大化土地期望值方面的效率。结果：轮伐期为50–75年，折现率为4％，与其他间伐方法相比，启发式树种选择始终提高了土地期望值。两种英雄启发式方法是计算效率最高的方法。四种蒙特卡洛启发式方法所需的计算量是英雄的2.8-3.4倍。遗传算法和禁忌搜索分别比英雄需要4.2–8.4和21–52倍的计算量。结论：稀疏处方的准确性受到林分测量质量的限制，生长和产量模型的准确性与启发式方法有关，而不是与启发式方法的选择有关。但是，启发式性能可能对所选模型敏感。