No abstract

The article deals with the problem of multi-criteria assessment and management of the implementation of multi-scenario multi-stage regional environmental projects. The proposed approach involves building a scenario network, each node of which corresponds to a set of activities of a certain stage. The choice of the arc of transition to the next stage (the option of continuing the project) is made on the basis of knowledge about the subject area of the project, represented by a system of linguistic variables and fuzzy production rules linking them. The recommended solution for choosing the continuation of the project is determined based on the fuzzy inference procedure. The final choice of the option to continue the implementation of the environmental project is made by the decision-maker on the basis of their own (often non-formalized) preferences. As an example of the application of the proposed approach, the task of managing regional forest conservation projects is considered. The use of the developed decision support tools for adaptive management of environmental projects makes it possible to increase the scientific validity of management decisions, and, consequently, to reduce costs and increase environmental efficiency in the implementation of these projects.

The article is devoted to the problems of improving digital intellectual tools for managing the implementation of socio-economic and technological programs aimed at developing the agro-industrial cluster of the regional economy. The aim of the work is to develop a procedure for forecasting the implementation of programs based on the data of the previous stages and knowledge, reflecting the specifics of agricultural production. To describe the indicators of the current and projected state of the regional agro-industrial complex, it is proposed to use the apparatus of the theory of linguistic variables, which makes it possible to use expert technologies for filling the knowledge base and allows us to take into account the high level of uncertainty characteristic of the agricultural market. The links between current and projected performance are represented by fuzzy production rules. The fuzzy inference procedure used in forecasting (based on the Mamdani algorithm) is built in the form of an interpreted fuzzy multilayer neural network. The preliminary results of using the developed procedure as part of a research prototype of an information-analytical system may indicate its effectiveness. The practical significance of the developed toolkit is due to the possibility of its use as a means of intellectual support for making scientifically grounded management decisions on the implementation (taking into account possible adjustments) of development programs for the regional agro-industrial complex.

No abstract