Coordination algorithms for managing large-scale projects

The article solves the problem of developing two coordination algorithms for managing large-scale projects. The first algorithm is designed to select the appropriate method of coordination when managing the projects under consideration. The algorithm is based on the thesis that the higher the threat of failure of the project plan, the higher the degree of centralization of project management should be, and the lower the threat, the less centralized the project management should be. With this approach, choosing the appropriate method of coordination actually comes down to assessing the threat level. Given that the concept of "level" is of a qualitative nature, it is proposed to characterize it by the function of belonging to gradations: "very high", "high", "medium"," low", and" zero", and to evaluate the current threat level, use the measure of hemming proximity, which uses the zero threat level as a reference. The second algorithm is designed to select optimal coordinating solutions based on the criterion of minimum deviation of the project from the specified target state. Its novelty and originality lies in the fact that, unlike the usual optimization approach, it is built on a combination of methods of full iteration and linear programming. This made it possible to correctly take into account the fact that coordinating and design solutions are inextricably linked with each other.

1. Novoseltsev V.I. Systems analysis: modern concepts. Voronezh: Publishing house "Quart". 2004: 320.

2. Milner B.Z. Organization theory: Textbook. M .: INFRA-M. 2002: 480.

3. Organizations: Management, Conflicts, Crises, Risks: A Textbook. Ed. S.A. Barkalov and V.I. Novoseltsev. Voronezh: Scientific book. 2009: 300.

4. Aliev R.A., Liberson M.I. Methods and algorithms for coordination in industrial control systems. M .: Science. 1987: 230.

5. Mesarovich M., Mako D., Takahara J. The theory of hierarchical multilevel systems. M .: Mir. 1973: 260.

6. Conflict and Risk Management Models: Monograph. Ed. D.A. Novikov. Voronezh: Scientific book. 2008: 495.

7. Fuzzy sets in management and artificial intelligence models. Ed. D. A. Pospelov. M .: Science. Ch. ed. physical-mat. lit. 1986: 312.

8. Kliger S.A., Kosolapov M.S., Tolstova Yu.N. Scaling for collection and analysis sociological information. M .: Science. 1978: 107.

9. V.A. Yadov Sociological research: methodology, program, methods. Samara: Publishing house of the Samara State University. 1995: 328.

10. Leonenkov A.V. Fuzzy modeling in MATLAB and fuzzyTECH. SPb: BHV Petersburg. 2005: 736.

11. Yakhyaeva G.E. Fuzzy Sets and Neural Networks: A Tutorial. M .: Internet University of Information Technologies; BINOMIAL. Knowledge laboratory. 2006: 316.

12. Novoseltsev V.I., Kochedykov S.S., Orlova D.E. Crohn's tensor analysis and its applications. Ed. V.I. Novoseltsev. Voronezh: IPC "Scientific book". 2017: 220.

13. Novoseltsev V.I., Orlova D.E. Coordination management as a way to resolve economic conflicts. Actual problems of innovation systems informatization and security. Mater. international scientific and practical conf. Voronezh, VIVT. 2014: 434-436.

14. Novoseltsev V.I., Orlova D.E. Features of active hierarchical management system with unclear criteria. Mater. all-Russian. scientific and technical Conf., dedicated to the 95th anniversary of the signal troops - VUNC Air Force "VVA im. Zhukovsky and Gagarin ". Voronezh. 2014: 256-260.

15. Orlova D.E. Novoseltsev V.I. Mathematical model and coordination algorithms for ensuring complex security of an organization. Journal of Physics: Conference Series Current Problems. International Conference «Applied Mathematics, Computational Science and Mechanics: Current Problems». 2018:012042.