Publications
2020
Kuzmina, Elena M.; Klochko, Oksana; Savina, Nataliia B.; Yaremko, Svetlana A.; Akselrod, Roman B.; Strauss, Christine
Risk analysis of the Company's activities by means of simulation Konferenzbeitrag
In: Pickl, Stefan Wolfgang; Lytvynenko, Volodymyr; Zharikova, Maryna; Sherstjuk, Volodymyr (Hrsg.): CITRisk 2020. Computational & Information Technologies for Risk-Informed Systems, S. 162–174, RWTH Aachen, 2020, (Publisher Copyright: Copyright textcopyright 2020 for this paper by its authors. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 1st International Workshop on Computational and Information Technologies for Risk-Informed Systems, CITRisk 2020 ; Conference date: 15-10-2020 Through 16-10-2020).
Abstract | BibTeX | Schlagwörter: Information technologies, Monte Carlo method, Probabilistic characteristics, Risks, Simulation
@inproceedings{379b349e6052444c8072300d27fd99f9,
title = {Risk analysis of the Company's activities by means of simulation},
author = {Elena M. Kuzmina and Oksana Klochko and Nataliia B. Savina and Svetlana A. Yaremko and Roman B. Akselrod and Christine Strauss},
editor = {Stefan Wolfgang Pickl and Volodymyr Lytvynenko and Maryna Zharikova and Volodymyr Sherstjuk},
year = {2020},
date = {2020-01-01},
booktitle = {CITRisk 2020. Computational & Information Technologies for Risk-Informed Systems},
volume = {2805},
pages = {162--174},
publisher = {RWTH Aachen},
series = {CEUR Workshop Proceedings},
abstract = {The use of simulation methods and modern information technologies increases competitiveness, management efficiency and eliminates possible risks in the company's activities. The use of the Monte-Carlo method is promising in simulation. The basis of the classical Monte-Carlo method is to obtain a large number of implementations of a random process, which is formed so that the probabilistic characteristics (mathematical expectations, probability of some events, probability of the trajectory in a certain area, etc.) are equal to the predetermined value of the problem. Construction of the model using this method should be based on the distribution of random variables in the studied process. The set of implementations can be used as some artificially obtained statistical material processed by methods of mathematical statistics. The author's development was the application in the classical Monte-Carlo method of generating samples of random variables with uniform and triangular distribution, as well as risk analysis of the company and forecasting for the future with greater probability using modern means of automating complex calculations based on high-level programming languages. The software implementation of the advanced Monte-Carlo method is performed using high-level object-oriented Python language tools that allow you to automate all stages of application of the Monte-Carlo method and store the results in a database. Strategic planning support tools based on computer simulation provide an opportunity to reflect complex nonlinear interactions in the business, assess the consequences of the implementation of various scenarios or predict further developments in the company.},
note = {Publisher Copyright: Copyright textcopyright 2020 for this paper by its authors. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 1st International Workshop on Computational and Information Technologies for Risk-Informed Systems, CITRisk 2020 ; Conference date: 15-10-2020 Through 16-10-2020},
keywords = {Information technologies, Monte Carlo method, Probabilistic characteristics, Risks, Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of simulation methods and modern information technologies increases competitiveness, management efficiency and eliminates possible risks in the company's activities. The use of the Monte-Carlo method is promising in simulation. The basis of the classical Monte-Carlo method is to obtain a large number of implementations of a random process, which is formed so that the probabilistic characteristics (mathematical expectations, probability of some events, probability of the trajectory in a certain area, etc.) are equal to the predetermined value of the problem. Construction of the model using this method should be based on the distribution of random variables in the studied process. The set of implementations can be used as some artificially obtained statistical material processed by methods of mathematical statistics. The author's development was the application in the classical Monte-Carlo method of generating samples of random variables with uniform and triangular distribution, as well as risk analysis of the company and forecasting for the future with greater probability using modern means of automating complex calculations based on high-level programming languages. The software implementation of the advanced Monte-Carlo method is performed using high-level object-oriented Python language tools that allow you to automate all stages of application of the Monte-Carlo method and store the results in a database. Strategic planning support tools based on computer simulation provide an opportunity to reflect complex nonlinear interactions in the business, assess the consequences of the implementation of various scenarios or predict further developments in the company.