Regulation of even-aged forest with adjacency constraints

The objective of this work is to present a model of forest regulation to include adjacency constraints and present an evaluation index that fits the characteristics of even-aged forest in Brazil. The models were constructed for a model farm with an area of effective planting of 3491 ha, divided into 135 management units. The regulation models were formulated as model I, including integer constraints for the management units. We used meta-heuristic genetic algorithm for solving the models. For comparison we formulated a classical model of forest regulation. The purpose was maximizing the Net Present Value (NPV). The application of the technique of genetic algorithm is efficient for solving models, built with constraints and objectives of socioeconomic and environmental character. A comparison of volume variation is discussed to satisfy the demand of production imposed on all models. The inclusion of the adjacency constraint has reduced the overall NPV at 8% compared to the classical model. The IHA has shown to be feasible and efficient for evaluating the environmental influences of forest management even-aged.

► The genetic algorithm was efficient.
► CAI can be used in even-aged forest.