Statement of the problem of managing the incentive system for the teaching staff of the university using a genetic algorithm
https://doi.org/10.46554/1993-0453-2023-3-221-48-54
Abstract
The relevance lies in the fact that in relation to modern trends, universities are faced with the need to optimize their costs and improve the remuneration system for the teaching staff. Thus, the problem posed can be formulated as a need to develop a model of the incentive system for the teaching staff of the university. In view of the above problem, the purpose of this work is to formulate a control problem in relation to the incentive system for the teaching staff of the university based on the construction of a genetic algorithm. The applied research methods are evolutionary modeling, genetic algorithm. The model proposed in the study was described in the terminology of evolutionary modeling. So, in its formalized form, the model of the incentive system of the teaching staff of the university consists of three points, the first of which shows the initial population, which reflects a set of alternative options for the distribution of incentive payments, allowances, bonuses among the university staff from among the teaching staff. In the second paragraph of the proposed model, it is indicated that the university monitoring system selects chromosomes (variants for the distribution of incentive payments, allowances, bonuses) that meet the criteria of the objective function and, on their basis, a new population is formed and a genetic algorithm is given directly. The third paragraph states that after the formation of a new population, information about it is transmitted to the external environment, which includes monitoring by the Ministry of Education and Science and the Treasury, which, after processing the information received, forms target indicators of the university for the next reporting period and thereby launching a new cycle of the genetic algorithm. In the second section of the article, the objective function is formulated and formalized, followed by the construction of an optimal control problem on its basis. As a conclusion, we can say that the model presented in the study can serve as a basis for a computer program in the future.
About the Author
P. A. Nikulin
Bryansk State Technical University
Russian Federation
Russian Federation
Pavel A. Nikulin – post-graduate student of the Digital Economy Department
Bryansk
References
1. Klyachko T.L., Novoseltsev A.V., Odoevskaya E.V. Lessons of the coronavirus pandemic and possible changes in the mechanism of financial support for the activities of universities // Questions of education. 2021. No. 1. Pp. 8–30.
2. Kureychik V.V., Bova V.V., Leshchanov D.V. Model of semantic search in knowledge management systems based on genetic procedures // Information technologies. 2017. No. 12. Pp. 876–883.
3. Kazakov P.V. Software system of multi-criteria optimization // Bulletin of the Bryansk State Technical University. 2019. No. 7 (80). Pp. 66–75.
4. Markevich A.V., Sidorenko V.G. Automation of labor resource allocation management // Informatization of education and science. 2019. No. 3 (43). Pp. 36–49.
5. Korotchenko A.G., Kumagina E.A., Smoryakova V.M. Introduction to multicriteria optimization. Nizhny Novgorod : Nizhny Novgorod State University, 2017. 55 p.
6. Zaitsev A.M., Dadykin V.S. Application of ontology tools in the process of marketing activity management of higher educational institutions // Bulletin of the North-Eastern Federal University named after M.K. Ammosov. Ser.: Economics. Sociology. Cultural studies. 2021. No. 3 (23). Pp. 6–10.
7. Grebennikova I.V. Optimization methods : textbook. Yekaterinburg : UrFU, 2017. 148 p.
8. Zaginailo M.V., Fathi V.A. Genetic algorithm as an effective tool of evolutionary algorithms // Innovations. Science. Education. 2020. No. 22. Pp. 513–518.
9. Prokhorova I.A., Averyanova S.S. Application of genetic algorithms in solving multicriteria problems // Science of SUSU : materials of the 72nd Scientific Conference. Chelyabinsk : SUSU Publishing Center, 2020. Pp. 112–120.
10. Kashtaeva S.V. Optimization methods : textbook. Perm : Prokrost, 2020. 84 p.
11. Nikulin P.A. Statement of management tasks in relation to the model of the faculty incentive system at the university // XXI century: results of the past and problems of the present plus. 2021. Vol. 10, No. 4 (56). Pp. 49–53.
Review
For citations:
Nikulin P.A. Statement of the problem of managing the incentive system for the teaching staff of the university using a genetic algorithm. Vestnik of Samara State University of Economics. 2023;(3):48-54. (In Russ.) https://doi.org/10.46554/1993-0453-2023-3-221-48-54
Views:
5
Email this article 