Abstract
In article the risk management algorithm in Gaussian stochastic system is describes. The model risk management represents an optimization problem. The risk management algorithm is realized on the basis of the barrier functions method. The features of this nonlinear programming problem are not the convexity of the accessible solution region and the presence of stochastic restrictions on the required risk. The software implementation of the algorithm in the form of a separate module is performed. Using the Monte Carlo statistical test method, the algorithm was investigated. The algorithm showed stable control. Its efficiency is proved. Results of a research are presented in article. Recommendations on practical application of the algorithm are given.
The study was supported by the Russian Foundation for Basic Research (project no. 17-01-00315).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vishnyakov, Ya.D., Radayev, N.N.: Common Theory of Risks, 2nd edn. Academy, Moscow (2008)
Akimov, V.A., Lesnykh, V.V., Radaev, N.N.: Riski v prirode, tehnosfere, obshhestve i ekonomike [Risks in nature, technosphere, society, and the economy]. Business Express, Moscow (2004)
Mun, J.: Modeling Risk, 2nd edn. Wiley, Hoboken (2010)
Scheule, H., Rosch, D. (eds.): Model Risk: Identification, Measurement, and Management. Risk Books, London (2010)
Madera, A.G.: Risks and Chances. Uncertainty, Forecasting, and Assessment. URSS, Moscow (2014)
Solozhentsev, E.D.: Stsenarnoe logiko-veroyatnostnoe upravlenie riskom v biznesei tekhnike [Scenario logic and probabilistic management of risk in business and engineering], 2nd edn. Business Press, Saint Petersburg (2006)
Ryabinin, I.A.: Nadezhnost’ i bezopasnost’ strukturno-slozhnykh sistem [Reliability and safety of the structural and composite systems]. Polytechnique, Saint Petersburg (2000)
Vorobyov, Yu.L., Malinetsky, G.G., Makhutov, N.A.: Management of risk and sustainable development: human measurement. News of higher education institutions. Appl. Nonlinear Dyn. 8(6), 12–26 (2000)
Gorelik, V.A., Zolotova, T.V.: General approach to modeling of risk management procedures and its application to stochastic and hierarchic systems. Manage. Large Syst. 37, 5–24 (2012). (in Russian)
Tyrsin, A.N.: About model operation of risk in the systems of critical infrastructures. In: Economic and technical aspects of safety of structural critical infrastructures: Materials of the international conference, pp. 205–208. URFU, Yekaterinburg (2015)
Tyrsin, A.N., Surina, A.A.: Monitoring of risk of multidimensional stochastic system as tools for research of sustainable development of regions. IOP Conf. Series Earth Environ. Sci. 1(124), 776–783 (2018)
Tyrsin, A.N., Surina, A.A.: Models of monitoring and management of risk in Gaussian stochastic systems. Series Nat. Tech. Sci. 23(124), 776–783 (2018). The Bulletin of the Tambov University
Panteleev, A.V., Letova, T.A.: Optimization methods in examples and tasks. 3rd prod. The Higher School, Moscow (2008)
Mikhaylov, G.A., Voytishek, A.V.: Numerical Statistical Model Operation. Monte-Carlo Methods. Academy, Moscow (2006)
Tyrsin, A.N., Kalev, O.F., Yashin, D.A., Surina, A.A.: Model of risk of a multidimensional stochastic system as tools of a research of the state of health of population. Syst. Anal. Manage. Biomed. Syst. 17(4), 948–957 (2018)
Surina, A.A.: The program of risk management of a multidimensional Gaussian stochastic system: certificate on the state filing of the computer program No. 2018661134; stat. 21.08.2018; publ. 03.09.2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Surina, A.A., Tyrsin, A.N. (2019). Risk Management in Gaussian Stochastic Systems as an Optimization Problem. In: Bykadorov, I., Strusevich, V., Tchemisova, T. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2019. Communications in Computer and Information Science, vol 1090. Springer, Cham. https://doi.org/10.1007/978-3-030-33394-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-33394-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33393-5
Online ISBN: 978-3-030-33394-2
eBook Packages: Computer ScienceComputer Science (R0)