Mathematical apparatus of quality assessment of complex systems operation: methods and algorithms

Kamil Z. Biliatdinov

Abstract


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 various 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 one element in its composition, and methods for assessing the probability of achieving the goal of functioning of a complex system, depending on time characteristics and the number of failures that occur during operation. In each technique, 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 proposed variant of presenting methods and algorithms allows to maximum rationally use them in software for assessment of effectiveness and quality of complex systems.


Full Text:

PDF

References


Biliatdinov K.Z. Complex methodology of technical systems quality assessment in the process of maintenance // Scientific and technical Volga region bulletin. No. 11, 2020. – P. 20 – 23. (in Russian)

Biliatdinov K.Z., Meniailo V.V. Modified DEA method and methodology of assessment of technical systems’ effectiveness // Information technologies, 11, 2020. P. 611-617.

Han P., Wang L., Song P. Doubly robust and locally efficient estimation with missing outcomes. Statistica Sinica. 2016. Vol. 26. No. 2. Pp. 691-719.

Kalimoldayev M., Abdildayeva A., Mamyrbayev O. Information system based on the mathematical model of the EPS. Open engineering. 2016. Vol. 6. No. 1. Pp. 464-469.

Zou G., Faber M.H., Gonzalez A. A holistic approach to risk-based decision on inspection and design of fatigue-sensitive structures // Engineering structures. 2020. Vol. ‏221. Article 110949.

Luetje A., Wohlgemuth V. Tracking Sustainability Targets with Quantitative Indicator Systems for Performance Measurement of Industrial Symbiosis in Industrial Parks. Administrative sciences. 2020. Vol. 10. No. 1.

Ratner S., Ratner P. Developing a strategy of environmental management for electric generating companies using DEA-methodology // Advances in Systems Science and Applications. 2017. Vol. 17(4). - Р. 78-92.

Banker R., Kotarac K., Neralić L. Sensitivity and stability in stochastic data envelopment analysis. The Journal of the Operational Research Society. 2015. Vol. 66. No. 1. Pp. 134-147.

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. Vol. 279(3). Р. 1053-1064.

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. Vol. 279(3). Р. 1053-1064.

Price M., Walker S., Wiley W. The Machine Beneath: Implications of Artificial Intelligence in Strategic Decision making. PRISM. 2018. Vol. 7. No. 4. P. 92-105.

Zhang Z., David J. Structural order measure of manufacturing systems based on an information-theoretic approach // Expert Systems with Applications. 2020. Vol. 158. Article 113636.

Karagiannis G. On structural and average technical efficiency. Journal of Productivity Analysis. 2015. Vol. 43. No. 3. Pp. 259-267.

Putz M., Wiene, T., Pierer A. A multi-sensor approach for failure identification during production enabled by parallel datamonitoring. CIRP annals-manufacturing technology. 2018. Vol. 67. No. 1. Pр. 491-494.

Yang R., Zheng W. Output-Based Event-Triggered Predictive Control for Networked Control Systems // IEEE transactions on industrial electronics. 2020. Vol. ‏67. № 12. - Р.10631-10640.

Dulá J.H. Computations in DEA // Pesquisa Operacional. 2002. Vol. 22, № 2. – P. 165–182.

Trevino M. Cyber Physical Systems: The Coming Singularity. PRISM. 2019. Vol. 8. No. 3. Pp. 2-13.

Gerami J. An interactive procedure to improve estimate of value efficiency in DEA // Expert Systems with Applications. 2019. № 137. Р. 29-45.

Banker R.D., Charnes A., Cooper W.W. Some models for estimating technical and scale efficiency in data envelopment analysis // Management Science. 1984. Vol. 30, № 9. – P. 1078 – 1092.

Chen J.-X. Overall performance evaluation: new bounded DEA models against unreachability of efficiency. The Journal of the Operational Research Society. 2014. Vol. 65. No. 7. Pp. 1120-1132.

Biliatdinov K.Z. Assessment of systems stability (Certificate of the state registration of software № 2020615328, 21.05.2020).

Biliatdinov K.Z. Realization of the method of rational processing of information resources and formation of information reserves of a system (Certificate of the state registration of software № 2020610335, 13.01.2020).

Biliatdinov K.Z. Analysis and assessment of systems effectiveness (Certificate of the state registration of software № 2020610389, 14.01.2020).

Duer S. Assessment of the operation process of wind power plant’s equipment with the use of an artificial neural network // Energies. 2020. № 13. Art. 2437.

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. Vol. 204. Article 107190.

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. Article e12594.

Baker J., Henderson S. The Cyber Data Science Process. The Cyber Defense Review. 2017. Vol. 2. No. 2. P. 47-68.


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность IT Congress 2024

ISSN: 2307-8162