Integrating risk preferences in forest harvest scheduling

Through a stochastic programming framework, risk preferences can be included in forest planning. The value of utilizing stochastic programming is always positive; however, the value depends on the information quality and risk preferences of the decision maker.

Abstract

Harvest scheduling requires decisions be taken based on imperfect information and assumptions regarding the future state of the forest and markets. The aim of this study is to incorporate elements of risk management into forest management, so that the decision maker can understand the risks associated with utilizing the imperfect data. Incorporation of uncertainty is done through stochastic programming. This allows for the decision maker’s attitude towards risk to be incorporated into the development of a solution. By means of a simple even-flow problem formulation, a method of using stochastic programming to incorporate explicit trade-off between objective function value and risk of not meeting the constraints has been developed. The different models highlight the importance of including uncertainty in management of forest resources. In general, as the decision maker becomes more risk averse, the incorporation of uncertainty into the model becomes more important. The use of stochastic programming allows for additional information to be included in the formulation, and this allows for the decision maker to account for downside risk.