Practice of Procedures Development of the Innovative Assessment of One Class of Complex Systems Functioning
Abstract
In accordance with generally accepted criteria of systems classification, the article describes peculiarities of one class of complex systems.
The article proposes new methods of assessment of quality and effectiveness of functioning of complex systems. The methods are developed for the purpose of evaluation and recording of the specificity of functioning of different systems, moreover, for conducting assessment in the conditions of increasing volume of various sources of information together with stochastic character of dynamics of structured and unstructured data about complex systems.
The article also presents a model and formulas of control algorithms in complex systems, where managerial decisions are made on the basis of quality assessment of functioning of complex systems and (or) their subsystems (elements) and taking into consideration influence of external environment.
The modified DEA method used for assessment of systems effectiveness presents a combination of a classical DEA method, calculation of correlation of dependence of indices’ values and application of veto coefficient.
The article presents the directions of improvement of methods for calculating the probabilistic characteristics of complex systems of varied physical nature based on the application of the methodology for assessing the probability of failure of a given number of elements of a complex system, depending on the probability of failure of one element in its composition during operation. On the basis of a systematic approach, a sequence for assessing the corresponding probabilistic characteristics has been developed for rational implementation in computer programs.
Methods of calculation of complex quality indices include basic formulas and formulated conditions of their application.
The article describes computer programs for implementation of the methods and gives examples of assessment of effectiveness and probabilistic characteristics of systems with the help of these programs.
The article outlines prospects of the application of the procedures of innovative quality assessment of functioning of one class of complex systems for the purpose of solving topical problems of systemic engineering.
The proposed variant of presenting methods and algorithms makes it possible to use them most rationally in software for assessment of effectiveness and quality of complex systems in conditions of restricted resources and time.
References
2. Biliatdinov K.Z., Meniailo V.V. Modified method DEA and methodology of technical systems effectiveness assessment. Informacionnye Tehnologii = Information Technologies. 2020; 26(11):611-617. (In Russ., abstract in Eng.) doi: https://doi.org/10.17587/it.26.611-617
3. Han P., Wang L., Song P.X.-K. Doubly robust and locally efficient estimation with missing outcomes. Statistica Sinica. 2016; 26(2):691-719. (In Eng .) doi: http://dx.doi.org/10.5705/ss.2014.030
4. Kalimoldayev M.N., Abdildayeva A.A., Mamyrbayev O.Zh. Information system based on the mathematical model of the EPS. Open Engineering. 2016; 6(1):464-469. (In Eng.) doi: https://doi.org/10.1515/eng-2016-0053
5. Zou G., Faber M.H., González A., Banisoleiman K. A holistic approach to risk-based decision on inspection and design of fatigue-sensitive structures. Engineering Structures. 2020; 221:110949. (In Eng.) doi: https://doi.org/10.1016/j.engstruct.2020.110949
6. Lütje A., Wohlgemuth V. Tracking Sustainability Targets with Quantitative Indicator Systems for Performance Measurement of Industrial Symbiosis in Industrial Parks. Administrative Sciences. 2020; 10(1):3. (In Eng.) doi: https://doi.org/10.3390/admsci10010003
7. Ratner S., Ratner P. Developing a Strategy of Environmental Management for Electric Generating Companies Using DEA-Methodology. Advances in Systems Science and Applications. 2017; 17(4):78-92. (In Eng.) doi: https://doi.org/10.25728/assa.2017.17.4.521
8. Baker J., Henderson S. The Cyber Data Science Process. The Cyber Defense Review. 2017; 2(2):47-68. (In Eng.)
9. Calabrese R., Osmetti S. A. A new approach to measure systemic risk: A bivariate copula model for dependent censored data. European Journal of Operational Research. 2019; 279(3):1053-1064. (In Eng.) doi: https://doi.org/10.1016/j.ejor.2019.06.027
10. Farrell M.J. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, Series A (General), Part III. 1957; 120(3):253-281. (In Eng.) doi: https://doi.org/10.2307/2343100
11. Price M., Walker S., Wiley W. The Machine Beneath: Implications of Artificial Intelligence in Strategic Decision making. PRISM. 2018; 7(4):92-105. Available at: https://www.jstor.org/stable/26542709 (accessed 04.02.2022). (In Eng.)
12. Banker R., Kotarac K., Neralić L. Sensitivity and stability in stochastic data envelopment analysis. Journal of the Operational Research Society. 2015; 66(1):134-147. (In Eng.) doi: https://doi.org/10.1057/jors.2012.182
13. Karagiannis G. On structural and average technical efficiency. Journal of Productivity Analysis. 2015; 43(3):259-267. (In Eng.) doi: https://doi.org/10.1007/s11123-015-0439-x
14. Putz M., Wiene, T., Pierer A., Hoffmann M. A multi-sensor approach for failure identification during production enabled by parallel data monitoring. CIRP Annals. 2018; 67(1):491-494. (In Eng.) doi: https://doi.org/10.1016/j.cirp.2018.04.079
15. Sinitsyn I.N., Shalamov A.S. Stochastic CALS-technologies for highly available aftersale product management systems. Sistemy vysokoj dostupnosti = Highly Available Systems. 2016; 12(2):13-27. Available at: https://www.elibrary.ru/item.asp?id=26495574 (accessed 04.02.2022). (In Russ., abstract in Eng.)
16. Sinitsyn I.N., Shalamov A.S. Development of Russian integrated logistic support management system for life cycle management: methodological aspects. Sistemy vysokoj dostupnosti = Highly Available Systems. 2016; 12(3):3-8. Available at: https://www.elibrary.ru/item.asp?id=27706562 (accessed 04.02.2022). (In Russ., abstract in Eng.)
17. Yang R., Zheng W.X. Output-Based Event-Triggered Predictive Control for Networked Control Systems. IEEE Transactions on Industrial Electronics. 2020; 67(12):10631-10640. (In Eng.) doi: https://doi.org/10.1109/TIE.2019.2958303
18. Dulá J.H. Computations in DEA. Pesquisa Operacional. 2002; 22(2):165-182. (In Eng.) doi: https://doi.org/10.1590/S0101-74382002000200005
19. Trevino M. Cyber Physical Systems: The Coming Singularity. PRISM. 2019; 8(3):2-13. Available at: https://www.jstor.org/stable/26864273 (accessed 04.02.2022). (In Eng.)
20. Gerami J. An interactive procedure to improve estimate of value efficiency in DEA. Expert Systems with Applications. 2019; 137:29-45. (In Eng.) doi: https://doi.org/10.1016/j.eswa.2019.06.061
21. Sinitsyn I.N. Interpolatonal analytical modeling in complex stochastic systems. Informatika i ee primenenija = Informatics and Applications. 2019; 13(1):2-8. (In Russ., abstract in Eng.) doi: https://doi.org/10.14357/19922264190101
22. Biliatdinov K.Z., Meniailo V.V. Methodology of quality assessment of systems in the sphere of stability of big technical objects. Vek kachestva = Age of Quality. 2020; (2):198-214. Available at: https://www.elibrary.ru/item.asp?id=43107559 (accessed 04.02.2022). (In Russ., abstract in Eng.)
23. Banker R.D., Charnes A., Cooper W.W. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science. 1984; 30(9):1078-1092. Available at: https://www.jstor.org/stable/2631725 (accessed 04.02.2022). (In Eng.)
24. Chen J.-X. Overall performance evaluation: new bounded DEA models against unreachability of efficiency. The Journal of the Operational Research Society. 2014; 65(7):1120-1132. Available at: https://www.jstor.org/stable/24503167 (accessed 04.02.2022). (In Eng.)
25. Duer S. Assessment of the operation process of wind power plant’s equipment with the use of an artificial neural network. Energies. 2020; 13(10):2437. (In Eng.) doi: https://doi.org/10.3390/en13102437
26. Kostogryzov A.I., et al. Standarty, metody i tehnologii sistemnoj inzhenerii [Standards, methods and technologies of systems engineering]. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2013; (9):14-33. Available at: https://www.elibrary.ru/item.asp?id=23020507 (accessed 04.02.2022). (In Russ.)
27. Kostogryzov A.I. Risk prediction for providing information quality in complex systems. Sistemy vysokoj dostupnosti = Highly Available Systems. 2016; 12(3):25-38. Available at: https://www.elibrary.ru/item.asp?id=27706565 (accessed 04.02.2022). (In Russ., abstract in Eng.)
28. Kostogryzov A.I., Nistratov A.A. About the Promising Directions of System Engineering Development. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2021; 17(2):223-240. (In Russ., abstract in Eng.) doi: https://doi.org/10.25559/SITITO.17.202102.223-240
29. Zatsarinny A.A., Kostogryzov A.I., Nistratov A.A. Priority directions of system engineering development, providing for the use of risk-oriented approach. IT-Standart = IT Standard. 2021; (4):23-37. Available at: https://www.elibrary.ru/item.asp?id=48225571 (accessed 04.02.2022). (In Russ., abstract in Eng.)
30. Shafik M.B., Chen H., Rashed G. Planning and reliability assessment to integrate distributed automation system into distribution networks utilizing binary hybrid PSO and GSA algorithms considering uncertainties. International Transactions on Electrical Energy Systems. 2020; 30(11):e12594. (In Eng.) doi: https://doi.org/10.1002/2050-7038.12594
31. Yizhen P., Yu W., Jingsong X., Yanyang Z. Adaptive stochastic-filter-based failure prediction model for complex repairable systems under uncertainty conditions. Reliability Engineering and System Safety. 2020; 204:107190. (In Eng.) doi: https://doi.org/10.1016/j.ress.2020.107190
32. Zhang Z., David J. Structural order measure of manufacturing systems based on an information-theoretic approach. Expert Systems with Applications. 2020; 158:113636. (In Eng.) doi: https://doi.org/10.1016/j.eswa.2020.113636
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