The article discusses the issues of developing a decision support system in the field of regional development management. The aim of the study is to develop an approach to strategic planning of the socio-economic development of the region with the reconciliation of modern decision-making support tools. The objectives of the research are to develop the concept of this approach, to form an iterative algorithm for correcting the values of target indicators and an algorithm for intellectual decision support. It is also required to describe the functioning of the proposed intelligent adaptive simulation model and to test it. As a result of the research, the key elements of an intelligent decision support system are presented, algorithms for determining control parameters are developed. A model has been developed that includes a number of important elements, among which a special place is occupied by blocks responsible for developing decision parameters. In particular, at the level of formation of the management system, an algorithm for correcting the values of target indicators is proposed based on an analysis of the degree of implementation of the set plan and an assessment of the availability of an unused resource, characterized by the presence of iterative mechanisms and allowing to determine the parameters for adjusting target indicators of regional development. In turn, for the formation of management decisions, a two-stage algorithm of intellectual decision support has been developed, based on the identification of typical situations, characterized by the use of fuzzy boundaries of affiliation and allowing to determine solutions appropriate to the situation. The approbation was carried out on data from the regions of the Volga Federal District. A quantitative forecast of changes in the values of parameters for three scenarios up to 2030 was obtained. The average accuracy of forecasts that were obtained using the proposed model of the region turned out to be higher than a similar forecast without an intelligent adaptive simulation model by 2.8 percentage points. The resulting model can be used as part of the justification of government regulation measures.
Низамутдинов et al. (Thu,) studied this question.