Conference on Applied Statistics in Agriculture

FUZZINESS IN FOREST SURVEY DESIGN OPTIMIZATION

When using optimization techniques to optimize a sampling with partial replacement design, it is often assumed that the following parameters are known exactly: 1) desired level of sampling error or total sampling cost for the survey; 2) variable costs; and 3) population variance and correlation coefficients. In practice, however, these parameters needed for finding the optimal design are only educated guesses. The parameters can be considered to be fuzzy. In this paper, brief consideration is given to the optimization of a sampling with partial replacement design using nonlinear programming techniques with fuzzy parameters. The basis of this method is to obtain the optimal solution by minimizing the objective function, subject to some restrictions, when the parameters that appear in both the objective function and restriction functions are fuzzy. The method is applied to a two-occasion continuous forest inventory.