Skip to main content
SpringerLink
Log in

Reinforcement Machine Learning Model for Sports Infrastructure Development Planning

  • Published:
Mathematical Models and Computer Simulations Aims and scope

Abstract

This paper considers the actual task of planning the rational development of sports infrastructure with limited resources. The development of a mathematical model for the evaluation of sports infrastructure projects and the schedule for their implementation are carried out. To evaluate projects, it is proposed to use methods of multicriteria decision analysis based on fuzzy preference areas. It is difficult to search for the optimal parameters of the proposed model due to the presence of binary variables that make the problem NP-hard. To find a solution close to the optimal one, a machine learning model with reinforcement is proposed. Software is developed that allows both ranking projects and determining the schedule for their implementation, taking into account the available resources and needs. The algorithmic and software solution based on a machine learning model with reinforcement is invariant with respect to the subject area and can also be used in other combinatorial optimization problems. Computational experiments are carried out for the proposed solution on the example of the problem of choosing regions for the construction of basketball courts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.

Similar content being viewed by others

REFERENCES

  1. A. V. Lotov and I. I. Pospelova, Multicriteria Decision Making Problems (MAKS Press, Moscow, 2008) [in Russian].

    Google Scholar 

  2. A. V. Dutov, V. A. Nesterov, V. A. Sudakov, and K. I. Sypalo, “Fuzzy preference domains and their use for selecting an electronic flight bag for flight crews,” J. Comput. Syst. Sci. Int. 57 (2), 230–238 (2018). https://doi.org/10.1134/S1064230718020065

    Article  MATH  Google Scholar 

  3. G. N. Levchenko, O. S. Zaytsev, N. A. Isakov, V. A. Cherepanov, and A. V. Gartsev, “Features of the specifics of forming the availability of mass sports services,” Aktual. Issled., No. 15 (18), 85–91 (2020).

  4. R. V. Fayzullin and T. N. Ivanova, “Method for calculating economic and social effectiveness of sports objects,” Probl. Ekon. Menedzhmenta, No. 11 (51), 83–88 (2015).

    Google Scholar 

  5. C. Murillo, J. M. Raya, and J. Bosch, “The economic importance of the sports sector and the economic impact of sporting events,” in Sports (and) Economics, Ed. by J. Garcia, FUNCAS Social and Economic Studies, Vol. 7 (Funcas, Madrid, 2019), pp. 425–446.

  6. E. Jelešković, M. Talović, I. Rađo, I. Mahmutović, H. Alić, A. Mašala, Š. Ormanović, A. Ćirić, D. Đedović, and K. Dizdarević, “Analysis of organizational structure and financial aspects of successful European clubs,” Sport Sci.: Int. Sci. J. Kinesiol. 9 (1), 41–45 (2016).

    Google Scholar 

  7. R. R. Zaripova and M. R. Sikhimbaev, “Evaluation of a real estate object by taking into account the social efficiency of its work (with the example of a sports facility),” Mezhdunar. Stud. Nauchn, Vestn., No. 5 (2017).

  8. T. R. Solomakhina and E. A. Bobrovskii, “Assessment of the impact of the provision of sports infrastructure on the ability of the population to go in for sports,” Mezhdunar. Zh. Prikl. Fundam. Issled., No. 3-1, 133–137 (2017).

  9. E. R. Akhmetshina, O. A. Ignatjeva, and I. M. Ablaev, “Tendencies and prospects of public-private partnership development in the field of physical culture and sport,” Eur. Res. Studies J. XX (2A), 422–430 (2017). https://doi.org/10.35808/ersj/650

    Article  Google Scholar 

  10. M. Gordienko, K. Ekimova, N. Glubokova, and E. Shuvalova, “Fiscal and investment indicators for evaluating professional sports facilities,” SHS Web Conf. 89, 04002 (2020). https://doi.org/10.1051/shsconf/20208904002

  11. O. V. Mkrtychev and V. B. Dorozhinskiy, “The analysis of the constructive solutions and the results of the structural analyses of the sports facilities,” Stroit.: Nauka Obraz. 8 (2 (28)), 16–27 (2018). https://doi.org/10.22227/2305-5502.2018.2.2

    Article  Google Scholar 

  12. G. N. Levchenko, O. S. Zaytsev, and A. V. Gartsev, “Algorithm for implementing project financing in the field of sports,” Aktual. Issled., No. 15 (18), 97–101 (2020).

  13. N. K. Zavriev, I. G. Pospelov, and L. Ya. Pospelova, “Investigation of economy mathematical models with system ECOMOD tools,” Mat. Model. 15 (8), 57–74 (2003).

    MATH  Google Scholar 

  14. B. N. Chetverushkin and V. A. Sudakov, “Factor modeling for innovative enterprises,” Math. Models Comput. Simul. 12 (6), 907–914 (2020). https://doi.org/10.1134/S2070048220060058

    Article  MathSciNet  MATH  Google Scholar 

  15. H. Dai, E. B. Khalil, Y. Zhang, B. Dilkina, and L. Song, “Learning combinatorial optimization algorithms over graphs,” in Proc. 31st International Conference on Neural Information Processing Systems (NIPS 2017) (Curran Associates, Red Hook, NY, 2017), pp. 6351–6361.

  16. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” (2017). https://doi.org/10.48550/arXiv.1707.06347

Download references

Funding

This study was carried out with the financial support of the Plekhanov Russian University of Economics.

Author information

Authors and Affiliations

  1. Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, Russia

    V. A. Sudakov

  2. Plekhanov Russian University of Economics, Moscow, Russia

    V. A. Sudakov, I. A. Belozerov & E. S. Prudkova

Authors
  1. V. A. Sudakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. A. Belozerov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. S. Prudkova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. A. Sudakov.

Ethics declarations

The authors declare that they have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sudakov, V.A., Belozerov, I.A. & Prudkova, E.S. Reinforcement Machine Learning Model for Sports Infrastructure Development Planning. Math Models Comput Simul 15, 608–614 (2023). https://doi.org/10.1134/S2070048223040178

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S2070048223040178

Keywords:

Search

Navigation