Evaluating the required scenario set size for stochastic programming in forest management planning: incorporating inventory and growth model uncertainty
Evaluating the required scenario set size for stochastic programming in forest management planning: incorporating inventory and growth model uncertainty
复制标题
评估森林管理规划中随机规划所需的情景集大小：纳入库存和增长模型的不确定性
DOI：
10.1139/cjfr-2014-0513
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
A. Kangas
中科院分区：
文献类型：
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作者：
K. Eyvindson;A. Kangas
制定未来利用森林资源的行动计划需要一种方法来预测森林随时间的发展。这些预测需要使用包含大量不确定性的库存数据和增长模型。这些不确定性影响预测的质量，如果不考虑这些不确定性，可能会导致选择次优的管理计划。为了解释和管理不确定性和相关风险，我们探索了随机规划的使用。随机规划通过求解森林未来发展的大量潜在情景，将不确定性融入到优化过程中。选择一组适当大小的场景涉及到可处理性问题和问题表示问题之间的权衡。在本文中，我们对这种权衡进行了分析。研究了两种情况，一种情况下只考虑库存数据的不确定性，另一种情况下两种情况都有增长。
Developing a plan of action for the future use of forest resources requires a way to predict the development of the forest through time. These predictions require the use of inventory data and growth models that contain a large number of uncertainties. These uncertainties impact the quality of the predictions, and if not accounted for, they can lead to the selection of a suboptimal management plan. To account for and manage the uncertainties and associated risk, we have explored the use of stochastic programming. Stochastic programming can integrate uncertainty into the optimization process by solving the problem for a large number of potential scenarios of the forests future development. The selection of an appropriately sized set of scenarios involves a trade-off between tractability issues and problem representation issues. In this paper, an analysis of the trade-offs is conducted. Two cases are studied, one in which only the uncertainty of the inventory data is included and a second in which both grow...