Оптимизация управления ресурсным обеспечением в региональной организационной системе на основе предиктивного анализа многолетней статистической информации
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Optimization of resource management in a regional organizational system based on predictive analysis of long-term statistical information

Gusev P. Y.,  Lomakov A. V.,  Lvovich Y. 

UDC 681.3
DOI: 10.26102/2310-6018/2024.47.4.007

  • Abstract
  • List of references
  • About authors

The article considers the rationale for the optimization approach to resource provision management in a regional organizational system, which is distinguished by the procedures for integrating the results of predictive analysis of long-term statistical information into the decision-making process. The limitations of methods of expert selection of management actions based on the analysis of the dynamics of changes in the system's performance indicators and the possibility of overcoming this limitation through a formalized representation of the dependence of the integral effect function on additional resource provision, which makes it possible to move on to the search for management solutions through optimization modeling, are shown. The formation of optimization models for the distribution of resource provision in a regional organizational system is considered according to three components: population groups, territorial entities, and time periods. For the first two components, management decisions are determined by setting and solving multi-alternative optimization problems. They allow one to determine promising subsets of population groups and territorial entities for which the need for additional resources determined by experts will give the greatest effect in future periods. Since management decisions contain an expert component along with a formalized choice, they are preliminary in nature. The final formalized decision is achieved by distributing preliminary estimates over time intervals using an optimization model of dynamic programming. It is proposed to use the results of predictive analysis in the form of prognostic models reflecting the data of statistical indicators when forming target functions of optimization models, which allows integrating them into the decision-making process when managing the distribution of resource provision in a regional organizational system.

1. Aleksandrova T.B., Kalinkin D.E., Pleshchinskaya V.Ya., Obraztsova E.N., Takhauov R.M., Khlynin S.M. Meditsinskaya statistika. Pokazateli i metody otsenki zdorov'ya naseleniya. Tomsk: SibMed; 2011. 126 p. (In Russ.).

2. Glushanko V.S., Timofeeva A.P., Gerberg A.A. Metodika izucheniya urovnya, chastoty, struktury i dinamiki zabolevaemosti i invalidnosti. Mediko-reabilitatsionnye meropriyatiya i ikh sostavlyayushchie. Vitebsk: Izdatel'stvo VGMU; 2016. 177 p. (In Russ.).

3. Schepin O.P. The regional aspects of health care development. Problems of Social Hygiene, Public Health and History of Medicine. 2014;22(5):3–7. (In Russ.).

4. Lobkova E.V., Petrichenko A.S. Managing the effectiveness of the regional health system. Regional Economics: Theory and Practice. 2018;16(2):274–295. (In Russ.). https://doi.org/10.24891/re.16.2.274

5. L'vovich Ya.E., L'vovich I.Ya., Choporov O.N. et al. Optimizatsiya tsifrovogo upravleniya v organizatsionnykh sistemakh. Voronezh: Publishing and Printing Center Nauchnaya kniga; 2021. 191 p. (In Russ.).

6. Kelleher J.D., Mac Namee B., D’Arcy A. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. The MIT Press; 2020. 856 p.

7. Gusev P.Yu. Analysis of the potential of predictive analytics in forecasting the condition of equipmen. News of the Tula State University. Technical Sciences. 2024;(4):103–108. (In Russ.).

8. Van Calster B., Wynants L., Timmerman D., Steyerberg E.W., Collins G.S. Predictive analytics in health care: how can we know it works? Journal of the American Medical Informatics Association. 2019;26(12):1651–1654. https://doi.org/10.1093/jamia/ocz130

9. L'vovich I.Ya., L'vovich Ya.E., Frolov V.N. Informatsionnye tekhnologii modelirovaniya i optimizatsii. Kratkaya teoriya i prilozheniya. Voronezh: Publishing and Printing Center Nauchnaya kniga; 2016. 444 p. (In Russ.).

10. L'vovich Ya.E. Mnogoal'ternativnaya optimizatsiya: teoriya i prilozheniya. Voronezh: Izdatel'stvo "Kvarta"; 2006. 415 p. (In Russ.).

11. Gafanovich E.Ya., Lomakov A.V., Lvovich A.I., Choporov O.N. Visual and predictive modeling of morbidity arterial hypertension in older age groups and their medical examination. Modeling, Optimization and Information Technology. 2024;12(2). (In Russ.). https://doi.org/10.26102/2310-6018/2024.45.2.014

12. L'vovich I.Ya. Prinyatie reshenii na osnove optimizatsionnykh modelei i ekspertnoi informatsii. Voronezh: Publishing and Printing Center Nauchnaya kniga; 2023. 232 p. (In Russ.).

Gusev Pavel Yuryevich
PhD, Associate Professor

Voronezh State Technical University

Voronezh, Russian Federation

Lomakov Andrew Vladimirovich

Voronezh Institute of High Technologies

Voronezh, Russian Federation

Lvovich Yakov
Doctor of Technical Sciences, Professor

Voronezh Institute of High Technologies

Voronezh, Russian Federation

Keywords: regional organizational system, management, resource provision, predictive analysis, forecasting, optimization, expert assessment

For citation: Gusev P. Y., Lomakov A. V., Lvovich Y., Optimization of resource management in a regional organizational system based on predictive analysis of long-term statistical information. Modeling, Optimization and Information Technology. 2024;12(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1710 DOI: 10.26102/2310-6018/2024.47.4.007 (In Russ).

23

Full text in PDF

Received 11.10.2024

Revised 21.10.2024

Accepted 25.10.2024