In a recent research article, Robinson et al. (2016) described a method of estimating uncertainty of harvesting outcomes by analyzing the historical yield to the associated prediction for a large number of harvest operations. We agree with this analysis, and consider it a useful tool to integrate estimates of uncertainty into the optimization process. The authors attempt to manage the risk using two different methods, based on deterministic integer linear programming. The first method focused on maximizing the 10th quantile of the distribution of predicted volume subject to area constraint, while the second method focused on minimizing the variation of total quantity of volume harvested subject to a harvest constraint. The authors suggest t...
In many recent studies, the value of forest inventory information in the harvest scheduling has been...
In many recent studies, the value of forest inventory information in the harvest scheduling has been...
International audienceAbstractKey messageThrough a stochastic programming framework, risk preference...
Because of the very high complexity of modern optimization models based on single trees, uncertainti...
Because of the very high complexity of modern optimization models based on single trees, uncertainti...
The paper shows how the aspects of uncertainty in spatial harvest scheduling can be embedded into a ...
Deciding upon a plan of action for a forest holding involves a significant amount of uncertainty. As...
Thesis (Ph.D.)--University of Washington, 2020Forest management planning aims at selecting the set o...
Deciding upon a plan of action for a forest holding involves a significant amount of uncertainty. As...
Graduation date: 2012Forest management is typically associated with a high degree of uncertainty, \u...
In this work the problem of optimal harvesting policy selection for natural resources management und...
Developing a plan of action for the future use of forest resources requires a way to predict the dev...
In British Columbia, the chief forester is legally required to set harvest levels within a dynamic f...
Developing a plan of action for the future use of forest resources requires a way to predict the dev...
In British Columbia, the chief forester is legally required to set harvest levels within a dynamic f...
In many recent studies, the value of forest inventory information in the harvest scheduling has been...
In many recent studies, the value of forest inventory information in the harvest scheduling has been...
International audienceAbstractKey messageThrough a stochastic programming framework, risk preference...
Because of the very high complexity of modern optimization models based on single trees, uncertainti...
Because of the very high complexity of modern optimization models based on single trees, uncertainti...
The paper shows how the aspects of uncertainty in spatial harvest scheduling can be embedded into a ...
Deciding upon a plan of action for a forest holding involves a significant amount of uncertainty. As...
Thesis (Ph.D.)--University of Washington, 2020Forest management planning aims at selecting the set o...
Deciding upon a plan of action for a forest holding involves a significant amount of uncertainty. As...
Graduation date: 2012Forest management is typically associated with a high degree of uncertainty, \u...
In this work the problem of optimal harvesting policy selection for natural resources management und...
Developing a plan of action for the future use of forest resources requires a way to predict the dev...
In British Columbia, the chief forester is legally required to set harvest levels within a dynamic f...
Developing a plan of action for the future use of forest resources requires a way to predict the dev...
In British Columbia, the chief forester is legally required to set harvest levels within a dynamic f...
In many recent studies, the value of forest inventory information in the harvest scheduling has been...
In many recent studies, the value of forest inventory information in the harvest scheduling has been...
International audienceAbstractKey messageThrough a stochastic programming framework, risk preference...