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
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
Bellman R. E. et al.: Certain problems of mathematical control theory. Publishing House of Foreign Literature, Moscow 1963.
Bellman R. E., Kalaba R. E.: Dynamic programming and modern control theory. Academic, New York 1965.
Bertsekas D. P.: Dynamic programming and Optical Control. Athena Scientific, 2017.
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
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].
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
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
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
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
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
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
Denardo E. V.: Dynamic Programming: Models and applications. Dover Publications 2003.
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
Forrester J.: Fundamentals of cybernetics of the enterprise (Industrial dynamics). Progress, Мoscow 1971.
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
Leggatt T. W.: The evolution of Industrial Systems. Croom Helm, London 1985.
Leontiev V.: Theoretical assumptions and nonobservable facts. Economy, ideology, politics 9, 1972, 15.
Mesarovic M., Takahara Y.: General systems theory: mathematical foundations. Academic Press, New York, San Francisco, London 1975.
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.
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
Opoitsev V. I.: Equilibrium and stability in models of collective behavior. Mir, Moscow 1977.
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
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
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
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
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
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
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
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
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
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.
Weijia D., Ginger Z., Jungmin L.: Optimal Aggregation of Consumer Ratings. NBER Working Paper No. 18567, 12–23.
Xinxin L.: Self-Selection and Information Role of Online Product Reviews. Information Systems Research 19(4), 2012, 56–64.
