A robust optimization approach protected harvest scheduling decisions against uncertainty
A robust optimization approach protected harvest scheduling decisions against uncertainty
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DOI：
10.1139/x08-175
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发表时间：
2009-02
影响因子：
2.2
通讯作者：
C. Palma;J. Nelson
中科院分区：
文献类型：
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作者：
C. Palma;J. Nelson
收获调度决策是在一个不确定的环境中，目前的建模技术，考虑不确定性造成严重的困难时，解决真实的问题。在本文中，我们描述了一个强大的优化方法，显式地考虑随机性的大部分模型系数，同时保持模型的计算易处理。当木材产量和两种产品的需求都不确定时，我们将该方法应用于采伐决策。由于不确定性系数必须是独立的、均匀的、对称分布的，所以我们只讨论预测模型估计误差引起的不确定性。该方法适用于245 090公顷的森林在加拿大不列颠哥伦比亚省。我们比较了收获决策和目标函数的变化时，鲁棒性的解决方案相对于确定性的解决方案。虽然概率界可以用来先验地定义违反约束的概率，但它们产生保守的解。因此我们...
Harvest scheduling decisions are made in an uncertain environment, and current modeling techniques that consider uncertainty impose severe difficulties when solving real problems. In this paper we describe a robust optimization methodology that explicitly considers randomness in most of the model coefficients while keeping the model computationally tractable. We apply the method to schedule harvest decisions when both timber yield and demand of two products are uncertain. Since uncertain coefficients must be independent, uniform, and symmetrically distributed, we only address uncertainty attributable to estimate errors of forecast models. The methodology was applied to a 245 090 ha forest in British Columbia, Canada. We compared the change in harvest decisions and objective function when robust solutions are implemented relative to deterministic solutions. Although probability bounds can be used to a priori define the probability of constraint violations, they produce conservative solutions. We therefore ...