Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Forest management planning usually takes place at the stand, enterprise or regional level. To maintain and conserve forest ecological processes, management operations should be transformed to an individual tree scale. This paper describes a method for supporting decision making at the level of individual trees following an approach based on close-to-nature forestry. An iterated conditional mode algorithm, as a relaxation of simulated annealing optimisation, was used to find an optimal solution. Using the principles of close-to-nature silviculture, a value function assigns management actions at the tree level. The reference conditions for optimisation are the decision-maker's preferences. The method was applied to an uneven-aged stand of Pinus sylvestris in the Guadarrama Mountains of Madrid (Spain) to find an optimal combination of individual trees to be harvested. An energy function is used to find an economic maximum for the remaining trees in the stand, in terms of the amount of low and high-quality timber to be harvested using the constraints: forest cover, biodiversity and regeneration. The optimal solution achieved an increased value for the stand of @?321.32/ha (@?96,396 for the 300ha forest), while the residual diameter distribution favoured ecosystem services and regeneration. Even though this type of model is adaptable to a variety of decision-maker preferences and optimisation constraints, its data requirements limit application to small, intensively managed properties.