OPRACOWANIE I BADANIE MODELU OPTYMALNEJ PRODUKCJI I ROZWOJU SYSTEMÓW PRZEMYSŁOWYCH Z UWZGLĘDNIENIEM WYKORZYSTANIA ZASOBÓW ZEWNĘTRZNYCH

Avrunin O. et al.: Classification of CT-brain slices based on local histograms. Proc. of SPIE 9816, 2015, 98161J.
DOI: https://doi.org/10.1117/12.2229040   Google Scholar

Azarova A.: Information Technologies and Neural Network Means for Building the Complex Goal Program „Improving the Management of Intellectual Capital”. Lecture Notes on Data Engineering and Communications Technologies 77, 2022, 534–547.
DOI: https://doi.org/10.1007/978-3-030-82014-5_36   Google Scholar

Bellman R. E. et al.: Certain problems of mathematical control theory. Publishing House of Foreign Literature, Moscow 1963.
  Google Scholar

Bellman R. E., Kalaba R. E.: Dynamic programming and modern control theory. Academic, New York 1965.
  Google Scholar

Bertsekas D. P.: Dynamic programming and Optical Control. Athena Scientific, 2017.
  Google Scholar

Borovska T, Hryshyn D.: Comparative Analysis of Methods for Optimizing Production Systems based on Hamiltonian and the Method of Optimal Aggregation. IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), 1, 2021, 345–348 [http://doi.org/10.1109/CSIT52700.2021.9648626].
DOI: https://doi.org/10.1109/CSIT52700.2021.9648626   Google Scholar

Borovska T. et al.: Searchless Intelligent System of Modern Production Control. IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), Zbarazh, Ukraine, 2020, 291–296 [http://doi.org/10.1109/CSIT49958.2020.9321842].
  Google Scholar

Borovska T.: Generalized model of optimal development, based on the integration of production and development subsystems. XII International Scientific and Technical Conference „Computer science and information technologies” CSIT’2017, Lviv, Ukraine, 2017, 446–449, 17353622 [http://doi.org/10.1109/STC-CSIT.2017.8098826].
DOI: https://doi.org/10.1109/STC-CSIT.2017.8098826   Google Scholar

Borovska T. et al.: Intelligent System of Modern Production Control Based on the Methodology of Optimal Aggregation, 2021, 291–296 [http://doi.org/10.1109/CSIT49958.2020.9321842].
DOI: https://doi.org/10.1109/CSIT49958.2020.9321842   Google Scholar

Borovska T. et al: Adaptive production control system based on optimal aggregation methods. Proc. of SPIE 10808, 2018, 108086O [http://doi.org/10.1117/12.2501520].
DOI: https://doi.org/10.1117/12.2501520   Google Scholar

Borovska T. et al.: Mathematical models of production systems development based on optimal aggregation methodology. Proc. of SPIE 10445, 2017, 104452P [http://doi.org/10.1117/12.2281222].
DOI: https://doi.org/10.1117/12.2281222   Google Scholar

Chertovskoy V., Tsehanovsky V.: Optimal model of manufacturing control system. Journal of Physics Conference Series 1864(1), 2021, 012096 [http://doi.org/10.1088/1742-6596/1864/1/012096].
DOI: https://doi.org/10.1088/1742-6596/1864/1/012096   Google Scholar

Das S. et al.: A production inventory model with partial trade credit policy and reliability. Alexandria Engineering Journal 60(1), 2021, 1325–1338 [http://doi.org/10.1016/j.aej.2020.10.054].
DOI: https://doi.org/10.1016/j.aej.2020.10.054   Google Scholar

Denardo E. V.: Dynamic Programming: Models and applications. Dover Publications 2003.
  Google Scholar

Fagin R., Kumar R., Sivakumar D.: Efficient similarity search and classification via rank aggregation. ACM SIGMOD International Conference on Management of Data, SIGMOD 2003 [http://doi.org/10.1145/872794.872795].
DOI: https://doi.org/10.1145/872757.872795   Google Scholar

Forrester J.: Fundamentals of cybernetics of the enterprise (Industrial dynamics). Progress, Мoscow 1971.
  Google Scholar

Koulouris A. et al.: Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing 126, 2021, 317–333.
DOI: https://doi.org/10.1016/j.fbp.2021.01.016   Google Scholar

Leggatt T. W.: The evolution of Industrial Systems. Croom Helm, London 1985.
  Google Scholar

Leontiev V.: Theoretical assumptions and nonobservable facts. Economy, ideology, politics 9, 1972, 15.
  Google Scholar

Mesarovic M., Takahara Y.: General systems theory: mathematical foundations. Academic Press, New York, San Francisco, London 1975.
  Google Scholar

Mukha Ap. A.: Control of the process of complex engineering systems and processes development. Characteristic features of FMEA-analysis application. Mathematical machine and systems 2, 2012, 168–176.
  Google Scholar

Murayama T., Devis P.: Optimal aggregation of noisy observations. Journal of Physics: Conference Series 233(1), 2003, 301–312 [http://doi.org/10.1145/872794.872795].
DOI: https://doi.org/10.1145/872794.872795   Google Scholar

Opoitsev V. I.: Equilibrium and stability in models of collective behavior. Mir, Moscow 1977.
  Google Scholar

Raymo M. et al.: A New Method for Food Production Analysis and Optimization Applied to Citrus Industry. Computer Aided Chemical Engineering 48, 2020, 2005–2010.
DOI: https://doi.org/10.1016/B978-0-12-823377-1.50335-9   Google Scholar

Romanyuk N. et al.: Microfacet distribution function for physically based bidirectional reflectance distribution functions. Proc. of SPIE 8698, 2012, 86980L [http://doi.org/10.1117/12.2019338].
DOI: https://doi.org/10.1117/12.2019338   Google Scholar

Romanyuk O. et al.: Method of anti-aliasing with the use of the new pixel model. Proc. of SPIE 9816, 2015, 981617 [http://doi.org/10.1117/12.2229013].
DOI: https://doi.org/10.1117/12.2229013   Google Scholar

Romanyuk S. et al.: New method to control color intensity for antialiasing. International Siberian Conference Control and Comm. – SIBCON, 2015 [http://doi.org/10.1109/SIBCON.2015.7147194].
DOI: https://doi.org/10.1109/SIBCON.2015.7147194   Google Scholar

Rüttimann B., Stockli M.: Going beyond triviality: The Toyota production system-lean manufacturing beyond Muda and Kaizen. J. Serv. Sci. Manag. 9, 2016, 140–149 [http://doi.org/10.4236/jssm.2016.92018].
DOI: https://doi.org/10.4236/jssm.2016.92018   Google Scholar

Rüttimann B.: Introduction to Modern Manufacturing Theory. Springer International Publishing AG 2018 [http://doi.org/10.1007/978-3-319-58601-4].
DOI: https://doi.org/10.1007/978-3-319-58601-4   Google Scholar

Skrynkovskyy R. et al.: Improvement of the express diagnostics of the production activity of the enterprise taking into account the method of determining the optimal production programs in the operational management system. Technology Audit and Production Reserves 6(44), 2018, 4–10 [http://doi.org/10.15587/2312-8372.2018.147968].
DOI: https://doi.org/10.15587/2312-8372.2018.147968   Google Scholar

Skrynkovskyy R. et al.: Improvement of the model of the innovative development of the production system of industrial enterprises. Reports on Research Projects 1/4(45), 2019, 53.
DOI: https://doi.org/10.15587/2312-8372.2019.159227   Google Scholar

Taylor C.: Dynamic programming and the curses of dimensionality. Applications of dynamic programming to agricultural decision problems. CRC Press, 2019.
DOI: https://doi.org/10.1201/9780429040917   Google Scholar

Tsybakov B.: Optimal Rates of Aggregation, Statistical Learning Theory and Stochastic Optimization. In: Saint-Flour E. D. et al.: Statistical learning theory and stochastic optimization: École d'eté de probabilités de Saint-Flour XXXI – 2001. 2004, 54–69.
  Google Scholar

Weijia D., Ginger Z., Jungmin L.: Optimal Aggregation of Consumer Ratings. NBER Working Paper No. 18567, 12–23.
  Google Scholar

Xinxin L.: Self-Selection and Information Role of Online Product Reviews. Information Systems Research 19(4), 2012, 56–64.
  Google Scholar