Optimal adaptive control of business planning processes based on network economic and mathematical modeling

The article discusses the problem of optimizing the adaptive control of business planning processes by an economic entity. The results of the work are based on a new method of network formalization and optimization of adaptive project management, using network economic and mathematical modeling and the principles of adaptive control. The paper describes a new optimization network economicand mathematical model. This model takes into account the possibilities of monitoring the states ofthe process under consideration and adaptive control of the implementation of a business project. Within the framework of the formed network model, a new method for solving the task of network formalization and optimization of adaptive control of business planning processes is proposed. When implementing the proposed method, a strategy for optimal adaptive control of business planning processes is formed. On its basis, the optimal time for the implementation of a business project and the optimal schedule for its implementation in general are calculated. The article describes the practical application of the proposed method for solving the considered optimization problem on a specific example of the implementation of business planning processes in the development of new dishes at a public catering enterprise. The results obtained in the work show the sufficient efficiency of the developed new method of formalization and optimization of adaptive control of business planning processes. Further development of this area of research can be associated with the development of an intelligent computer system for optimizing the adaptive control of business planning processes and the creation of appropriate tools to support the adoption of management decisions by business entities during their implementation. © 2020 Author(s).This work was supported by the Russian Basic Research Foundation, project no.18-01-00544 “Problems of attainability, control, estimation in dynamical systems with impulse control and uncertainty.