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In this paper, we present an approach employing multiobjective optimization to support decision making in forest management planning under risk. The primary objectives are biodiversity and timber cash flow, evaluated from two perspectives: the expected value and the value-at-risk (VaR). In addition, the risk level for both the timber cash flow and biodiversity values are included as objectives. With our approach, we highlight the trade-off between the expected value and the VaR, as well as between the VaRs of the two objectives of interest. We employ an interactive method in which a decision maker iteratively provides preference information to find the most preferred management plan and learns about the interdependencies of the objectives at the same time. The method is illustrated with a case study in which biodiversity is assessed through an index calculated from the characteristics of the forest. Uncertainty is included both through modifying the input data according to the accuracy of current inventory methods and through growth model errors. This uncertainty is described using a set of 25 scenarios. Involving multiple components of risk is a highly relevant approach in multiobjective forestry; however, estimation of the uncertainty of biodiversity needs further attention. [ABSTRACT FROM AUTHOR]

Some ideas of neuro-dynamic programming (NDP) are illustrated by considering the problem of optimally managing a forest stand under uncertainty. Because reasonable growth models require state information such as height (or age), basal area, and stand diameter, as well as an indicator variable for treatments that have been performed on the stand, they can easily lead to very large state spaces that include continuous variables. Realistic stand management policies include silvicultural options such as pre-commercial and commercial thinning as well as post-harvest treatments. We are interested in problems that are stochastic in their basic growth dynamics, in market prices, and in disturbances, ranging from insects to fire to hurricanes. NDP algorithms are appropriate for problems with large dimensions that may lack a simple model of dynamics and stochastic processes. This paper looks at applying these ideas in the context of a multispecies model. Results show that policies obtained using NDP are optimal within a 95% confidence interval or better. The set of states and controls incorporated into our NDP model allows us to develop optimal policies with a level of detail not typically seen in the forestry literature. [ABSTRACT FROM AUTHOR]

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 growth model and inventory data uncertainties are included. The impact of increasing the number of scenarios on the problem representation is examined through a simple even-flow problem. [ABSTRACT FROM AUTHOR]