Models of optimal innovation development of production systems

Authors

  • Таиса Николаевна Боровская Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021, Ukraine https://orcid.org/0000-0002-5308-4872
  • Ирина Сергеевна Колесник Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021, Ukraine
  • Виктор Андреевич Северилов Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021, Ukraine
  • Павел Викторович Северилов Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2014.28030

Keywords:

modeling, production function, development, innovations, binary operator, optimal aggregation

Abstract

Formulation and solution of the problem of optimal aggregation of elements of the production system “innovations, development, production” by equivalent optimal element were presented. Production system and its elements are considered as technological resource converters. An optimal aggregation methodology, which integrates the equivalent transformations of structures of production systems and sub-optimization of subsystems was used. Generalized models of production functions - parameterized and stochastic were developed and studied. Stochastic models of parametric relations among the elements “innovations”, “development”, “production” were developed and studied. Theoretical justification of these models was performed. The new task of developing a ternary operator of optimal aggregation of the structure “innovations”, “development”, “production” was solved. Optimization variables are system resource allocation among the subsystems. The result of the operator  work is the optimal equivalent production function - a data structure, in which in addition to the values of the function and appropriate resource allocations, data from previous aggregations can be preserved. The new result of the work is the information technology of developing the optimal aggregation operator for sequential structures with parametric relations in the environment of mathematical packages. The studies on the developed model, the results of which have shown the possibility of using a model of the aggregated system “innovations, development, production” for decision support were carried out.

Author Biographies

Таиса Николаевна Боровская, Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021

Assistant Professor, Cand. Sc. (Eng.)

Department of Computer Control Systems

Ирина Сергеевна Колесник, Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021

Assistant Professor, Cand. Sc. (Eng.)

Department of Computer Science

Виктор Андреевич Северилов, Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021

Assistant Professor, Cand. Sc. (Eng.)

Павел Викторович Северилов, Vinnitsa National Technical University Khmelnytske shosse 95, Vinnitsa, Ukraine, 21021

Aspirant

Department of Computer Control Systems

References

  1. 1. Borovska, Т. М., Kolesnik, I. C., Severilov, V. A. (2009). Optimal aggregation method in optimization problems. Vinnitsa, Ukraine: Universum Vinnitsa, 229.

    2. Borovska, Т. М., Badera, S. P., Severilov, V. A., Severilov, P. V. (2009). Modeling and optimization processes of production systems with the use of external resources and the effects of development. Vinnitsa, Ukraine: VNTU, 255.

    3. Borovska, Т. М., Severilov, V. A., Badera, S. P., Kolesnik, I. C. (2009). Modeling the tasks of management of investments. Vinnitsa, Ukraine: VNTU, 178.

    4. Vasylska, М. V., Kolesnik, I. C., Severilov, V. A. (2011). Models-predictors: problems of development and the adequacy. Visnyk of Vinnytsia Politechnical Institute, 4, 114–121.

    5. Bellman, R. E., Kalaba, R. E. (1969). Dynamic programming and modern control theory. Moscow, USSR: Science, 131.

    6. Forrester, J. W. (1971). Basics of Cybernetics enterprises (Industrial Dynamics). Moscow, USSR: Progress, 340.

    7. Eklund, I. (1983). Elements of Mathematical Economics. Moscow, USSR: World, 248.

    8. Peschel, М. (1981). Modeling of signals and systems. Moscow, USSR: World, 302.

    9. Mesarovich, M., Mako, D., Takahara, I. (1973). The theory of hierarchical multilevel systems. Moscow, USSR: World, 344.

    10. Fagin, R., Kumar, R., Sivakumar, D. (2003). Efficient similarity search and classification via rank aggregation. Proceedings of the 2003 ACM SIGMOD international conference on on Management of data – SIGMOD '03. Association for Computing Machinery (ACM), 301–312. doi:10.1145/872794.872795.

    11. Kelly, K. (1998). New Rules for the New Economy. 10 radical strategies for a connected world. Viking Penguin, 179.

    12. Kondratieff, N. D., Stolper, W. F. (1935, November). The Long Waves in Economic Life. The Review of Economics and Statistics, № 17(6), 105-115. doi:10.2307/1928486.

    13. Kuznets, S. (1930). Secular movements in production and prices. Their nature and their Bearing upon Cyclical Fluctuations. Boston: Houghton Mifflin, 536.

    14. Jantsch, E. (1970). Technological forecasting in perspective. Moscow, USSR: Progress, 569.

Published

2014-10-24

How to Cite

Боровская, Т. Н., Колесник, И. С., Северилов, В. А., & Северилов, П. В. (2014). Models of optimal innovation development of production systems. Eastern-European Journal of Enterprise Technologies, 5(2(71), 42–50. https://doi.org/10.15587/1729-4061.2014.28030