Оптимизация процессов мониторинга показателей исследуемых проблем

Kirill Vyacheslavovich Gusev; Alexander Savelievich Leontiev

doi:10.25559/SITITO.019.202302.419-429

Kirill Vyacheslavovich Gusev MIREA – Russian Technological University http://orcid.org/0000-0003-0047-8373
Alexander Savelievich Leontiev MIREA – Russian Technological University http://orcid.org/0000-0003-3673-2468

DOI: https://doi.org/10.25559/SITITO.019.202302.419-429

Abstract

In this paper, a systematic analytical approach is considered to organize information support for monitoring a set of indicators of the analyzed problem when solving a changing set of tasks of the problem under study to ensure the necessary relevance and completeness of the information used. A generalized statement of the problem being solved, an algorithm for compressing the space of indicators characterizing the analyzed problem, its decomposition into a set of tasks (topics), the definition of a set of indicators of the working set describing the problem under study are considered. Formulas for the intensity of the indication of the parameters of the working set and the assessment of their relevance are given. At the same time, the priorities of the tasks (topics) into which the analyzed problem is decomposed, the intensity of the loss of relevance of the indicators of the working set and the characteristic matrices of the probabilities of the indicators of the working set being included in the set of topics under consideration when organizing monitoring are taken into account. The formulation of an optimization problem is given for the selection of characteristic matrices of probabilities of the indicators of the working set entering into the set of topics of the problem under study in the organization of monitoring, which ensure the maximum relevance of the information used. It is noted that this optimization problem is reduced to a combinatorial problem of extremely large dimension. This dictates the need to develop heuristic algorithms for solving this optimization problem. The use of the proposed approach makes it possible to structure emerging problems on a single unified basis, take into account the impact of new disturbing influences, change the evaluation criteria for the importance of the tasks being solved, and when organizing monitoring of indicators of the problem under study, provide rational information support in the process of analyzing various scenarios for the development of the analyzed problems in the preparation and adoption of management decisions.

Author Biographies

Kirill Vyacheslavovich Gusev, MIREA – Russian Technological University

Senior Lecturer of the Chair of Mathematical Support and Standardization of Information Technologies, Institute of Information Technologies

Alexander Savelievich Leontiev, MIREA – Russian Technological University

Senior Researcher, Associate Professor of the Chair of Mathematical Support and Standardization of Information Technologies, Institute of Information Technologies, Cand. Sci. (Eng.)

References

1. Pursiainen C.H., et al. Critical infrastructure resilience index. In: Walls L., Revie M., Bedford T. (eds.) Risk, Reliability and Safety: Innovating Theory and Practice. Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016). London: CRC Press; 2016. p. 2183-2189. https://doi.org/10.1201/9781315374987-330
2. Labaka L., Hernantes J., Sarriegi J.M. Resilience framework for critical infrastructures: An Empirical Study in a Nuclear Plant. Reliability Engineering and System Safety. 2015;(141):92-105. https://doi.org/10.1016/j.ress.2015.03.009
3. Zarubin V.S., Kuvyrkin G.N. Special features of mathematical modeling of technical instruments. Mathematical Modeling and Computational Methods. 2014;(1):5-17. (In Russ., abstract in Eng.) EDN: STHFYV
4. Funke J. It Requires More Than Intelligence to Solve Consequential World Problems. Journal of Intelligence. 2021;9(3):38. https://doi.org/10.3390/jintelligence9030038
5. Stroev V.V., Tikhonov A.I. Development of a methodological approach to the procedure for assessing the quality and effectiveness of the management decision making mechanism. Vestnik Universiteta. 2022;1(11):26-32. (In Russ., abstract in Eng.) https://doi.org/10.26425/1816-4277-2022-11-26-32
6. Tolstova Yu.N. Mathematical modeling of social processes in sociology. Sotsiologicheskie Issledovaniia = Sociological Studies. 2018;(9):104-112. (In Russ., abstract in Eng.) https://doi.org/10.31857/S013216250001965-4
7. Nazarov A.A. Approaches to the selection of rational parameters of elements of the monitoring system of man-made emergencies in the construction of a comprehensive system of life safety of the population. Technosphere safety. 2021;(1):123-132. (In Russ., abstract in Eng.) EDN: CGSFNW
8. Andrianova E.G., Golovin S.A., Zykov S.V., Lesko S.A., Chukalina E.R. Review of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systems. Russian Technological Journal. 2020;8(4):7-45. (In Russ., abstract in Eng.) https://doi.org/10.32362/2500-316X-2020-8-4-7-45
9. Mikhailov V.M. The effectiveness of monitoring as a necessary condition for the adoption of correct decisions on the security f technosphere. Russian Technological Journal. 2020;8(2):23-32. (In Russ., abstract in Eng.) https://doi.org/10.32362/2500-316X-2020-8-2-23-32
10. Poltavskij A.V., Zhumabaeva A.S., Ajzharikov K.A., Pivkin A.V., Telegin A.M. Decision making concept to create complex technical systems. Reliability & Quality of Complex Systems. 2016;(2):74-84. EDN: VXMQHF
11. Kostogryzov A.I., Stepanov P.V., Nistratov A.A., Grigoriev L.I., Cherviakov L.M. Risk Prediction for Providing Information Quality in Complex Systems. Highly available systems. 2016;12(3):25-38. (In Russ., abstract in Eng.) EDN: XIHWLD
12. Leontiev A.S., Golovin S.A., Gusev K.V. Probabilistic Network Models for the Study of Typical Technological Schemes of Information Processing in Analytical Centers. Modern Information Technologies and IT-Education. 2022;18(3):516-527. (In Russ., abstract in Eng.) https://doi.org/10.25559/SITITO.18.202203.516-527
13. Gusev K.V., Leontiev A.S., Puchkova M.A. Analytical model for the analysis of local computer networks with a marker access method. Obshhestvo = Society. 2022;(1-1):38-42. (In Russ., abstract in Eng.) EDN: QNCBVC
14. Leontev A.S., Timoshkin M.S. Mathematical models for evaluating reliability indicators to study the probabilistic and temporal characteristics of multi-machine complexes with regard to failures. International Research Journal. 2023;(1):18. (In Russ., abstract in Eng.) https://doi.org/10.23670/IRJ.2023.127.27
15. Biryukova A.A., Gusev K.V., Leontiev A.S. A method of supporting managerial decision-making in crisis situations based on automated management systems. Informatization and communication. 2022;(6):65-74. (In Russ., abstract in Eng.) https://doi.org/10.34219/2078-8320-2022-13-6-65-74
16. Klimenko I.S., Sharapova L.V. The general problem of decision-making and the phenomenon of uncertainty. Vestnik of Russian New University. Series Complex systems: models, analysis, management. 2019;(3):44-58. (In Russ., abstract in Eng.) https://doi.org/10.25586/RNU.V9187.19.03.P.044
17. Chebakov S.V., Serebryanaya L.V. Algorithm of decision given combinatorial tasks on the base of many-critarial optimization model. Doklady BGUIR. 2015;(4):16-22. Available at: https://doklady.bsuir.by/jour/article/view/505/505 (accessed 16.01.2023). (In Russ., abstract in Eng.)
18. Martyushev N.V., Malozyomov B.V., Sorokova S.N., Efremenkov E.A., Valuev D.V., Qi M. Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport. Mathematics. 2023;11(15):3317. https://doi.org/10.3390/math11153317
19. Ahn E., Kang H. Introduction to systematic review and meta-analysis. Korean Journal of Anesthesiology. 2018;71(2):103-112. https://doi.org/10.4097/kjae.2018.71.2.103
20. Toufah N., Jaegler A., Kacem T. The Business Process Management: A Successful Tool for Enhancing Moroccan Firms social and financial Performance. Projectics / Proyéctica / Projectique. 2020;26(2):95-114. https://doi.org/10.3917/proj.026.0095
21. Dehghani M., Trojovská E., Trojovský P., Malik O.P. OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics. 2023;8(6):468. https://doi.org/10.3390/biomimetics8060468
22. Toscano R., Lyonnet P. Heuristic Kalman Algorithm for Solving Optimization Problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2009;39(5):1231-1244. https://doi.org/10.1109/TSMCB.2009.2014777
23. Gai W., Qu C., Liu J., Zhang J. A novel hybrid meta-heuristic algorithm for optimization problems. Systems Science & Control Engineering. 2018;6(3):64-73. https://doi.org/10.1080/21642583.2018.1531359
24. Tong Z., Chen H., Liu B., Cai J., Cai S. A novel intelligent hyper-heuristic algorithm for solving optimization problems. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology. 2022;42(6):5041-5053. https://doi.org/10.3233/JIFS-211250
25. Nyarko E.K., Cupec R., Filko D. A Comparison of Several Heuristic Algorithms for Solving High Dimensional Optimization Problems. International Journal of Electrical and Computer Engineering Systems. 2014;5(1):1-8. Available at: https://ijeces.ferit.hr/index.php/ijeces/article/view/839 (accessed 16.01.2023).