In the doctoral dissertation, we improved several multi-criteria decision-making methods to work with fuzzy numbers and in group decision-making. We introduced the DEMATEL method of finite sum of influences (FSI DEMATEL) to determine the mutual influences of elements in a system, where the finite sum of terms is used in the calculation of the total influence matrix of elements, ensuring convergence. With the FSI DEMATEL method, the influence of each element is considered only once, providing a more appropriate evaluation of mutual influences among elements. We adapted the WINGS method, which takes into account both influences and the strength of elements, to a fuzzy version called FSI WINGS (finite sum of influences WINGS). We upgraded the BWM method of group multi-criteria decision-making into a new method for aggregating individual assessments into group assessments (Similarity-awarded Group Best-Worst Method, SGBWM). In SGBWM, the similarity between individual decision-makers in choosing the best alternative is awarded when aggregating individual weights into group weights. We analyze the performance of all methods with examples. We set up a multi-criteria model for group decision-making for selecting the best alternative for forest management, which considers fuzzy information. The criteria were forest functions, the sub-criteria were risks, and we chose among four alternatives. The criteria were evaluated using the fuzzy FSI WINGS method, the sub-criteria with the fuzzy SGBWM method, and the alternatives with the fuzzy R-TOPSIS method. Various expert groups evaluated the model. The results show that there are no significant differences in the importance of individual forest functions. The greatest risk to forest management is climate change. The best alternative was found to be alternative 3 – monitoring and regulation.
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