Construction of algorithms for solving the inverse problem when using indicators in several calculation functions

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.251545

Keywords:

inverse problem, economic analysis, inverse calculations, optimization problem, iterative algorithm

Abstract

This paper reports a solution to the inverse problem when using indicators in several calculation functions. Such problems arise during the formation of a multi-level scorecard; solving them makes it possible to determine the value of arguments in order to achieve the specified value of the resulting indicator of each level. Thus, the characteristics of an economic object can be defined in order to achieve the specified indicators of its functioning. Optimization models are given in the presence of various types of conditions for achieving the result. In contrast to existing methods, the approach based on building nonlinear programming models makes it possible to solve the inverse problem for the case where several indicators are used in different calculation functions. Algorithms for solving the inverse problem have been constructed, involving the transformation of constraints and the use of an iterative procedure based on inverse calculations. For the case of using coefficients of relative importance, two techniques of solving the problem have been considered: the formation of a single model for subtasks and the adjustment of the solution to subtasks while minimizing the sum of squares of argument changes. In comparison with the existing method, the proposed algorithms have made it possible to derive a solution with a greater correspondence of the changes in the arguments to the coefficients of relative importance. A solution to the inverse problem has been considered related to the formation of marginal profit of an enterprise in the presence of two points of sale and three types of products, as well as the joint formation of revenue and cost. The results of this study could prove useful to specialists in the field of decision-making in the economy and to developers of software decision support systems that include functions for solving inverse and optimization problems.

Author Biography

Ekaterina Gribanova, Tomsk State University of Control Systems and Radioelectronics

Candidate of Technical Sciences, Associate Professor

Department of Automated Control Systems

References

  1. Klyuchinskiy, D. V., Novikov, N. S., Shishlenin, M. A. (2021). CPU-time and RAM memory optimization for solving dynamic inverse problems using gradient-based approach. Journal of Computational Physics, 439, 110374. doi: https://doi.org/10.1016/j.jcp.2021.110374
  2. Bai, Y., Chen, W., Chen, J., Guo, W. (2020). Deep learning methods for solving linear inverse problems: Research directions and paradigms. Signal Processing, 177, 107729. doi: https://doi.org/10.1016/j.sigpro.2020.107729
  3. Shananin, A. A. (2018). Inverse Problems in Economic Measurements. Computational Mathematics and Mathematical Physics, 58 (2), 170–179. doi: https://doi.org/10.1134/s0965542518020161
  4. Zhang, W., Wang, S., Hou, L., Jiao, R. J. (2021). Operating data-driven inverse design optimization for product usage personalization with an application to wheel loaders. Journal of Industrial Information Integration, 23, 100212. doi: https://doi.org/10.1016/j.jii.2021.100212
  5. Silkina, G. Yu., Pereverzeva, A. A. (2016). Integration of the balanced scorecard and the method of reverse calculation as an analytical tool for company effectiveness management. St. Petersburg State Polytechnical University Journal. Economics, 3 (245), 258–267. doi: https://doi.org/10.5862/je.245.24
  6. Vatul'yan, A. O. (2007). Obratnye zadachi v mekhanike deformiruemogo tverdogo tela. Moscow: Fizmatlit, 224.
  7. Zheng, G.-H., Zhang, Q.-G. (2018). Solving the backward problem for space-fractional diffusion equation by a fractional Tikhonov regularization method. Mathematics and Computers in Simulation, 148, 37–47. doi: https://doi.org/10.1016/j.matcom.2017.12.005
  8. Odintsov, B. E. (2004). Obratnye vychisleniya v formirovanii ekonomicheskih resheniy. Moscow: Finansy i statistika, 192.
  9. Demin, D. (2017). Synthesis of optimal control of technological processes based on a multialternative parametric description of the final state. Eastern-European Journal of Enterprise Technologies, 3 (4 (87)), 51–63. doi: https://doi.org/10.15587/1729-4061.2017.105294
  10. Yang, X.-J., Wang, L. (2015). A modified Tikhonov regularization method. Journal of Computational and Applied Mathematics, 288, 180–192. doi: https://doi.org/10.1016/j.cam.2015.04.011
  11. Park, Y., Reichel, L., Rodriguez, G., Yu, X. (2018). Parameter determination for Tikhonov regularization problems in general form. Journal of Computational and Applied Mathematics, 343, 12–25. doi: https://doi.org/10.1016/j.cam.2018.04.049
  12. Gao, G., Han, B., Tong, S. (2022). A fast two-point gradient algorithm based on sequential subspace optimization method for nonlinear ill-posed problems. Mathematics and Computers in Simulation, 192, 221–245. doi: https://doi.org/10.1016/j.matcom.2021.09.004
  13. Tsvetkov, M. A. (2007). "Vozvratno-setevoy" metod sovershenstvovaniya struktury kreditno-depozitnoy bazy kommercheskih bankov. Ekonomika i upravlenie, 1, 139–141.
  14. Gribanova, E. (2020). Development of iterative algorithms for solving the inverse problem using inverse calculations. Eastern-European Journal of Enterprise Technologies, 3 (4 (105)), 27–34. doi: https://doi.org/10.15587/1729-4061.2020.205048
  15. Ye, N., Roosta-Khorasani, F., Cui, T. (2019). Optimization Methods for Inverse Problems. MATRIX Book Series, 121–140. doi: https://doi.org/10.1007/978-3-030-04161-8_9
  16. Trunov, A. N. (2015). Modernization of means for analyses and solution of nonlinear programming problems. Quantitative Methods in Economics, 16 (2), 133–141. Available at: https://www.infona.pl/resource/bwmeta1.element.desklight-cba3a7e2-4c09-42c0-a6f5-a80839ba1e95/content/partContents/599a21a3-da58-3b64-9f0b-e7de96363cb2
  17. Gribanova, E. (2020). Algorithm for solving the inverse problems of economic analysis in the presence of limitations. EUREKA: Physics and Engineering, 1, 70–78. doi: https://doi.org/10.21303/2461-4262.2020.001102
  18. Gribanova, E. (2021). An Iterative algorithm for solving inverse problems of economic analysis using weighting factors. Advances in Engineering Research. Vol. 43. New York: Nova Science Publishers, 49–79.
  19. Gribanova, E. (2019). Development of a price optimization algorithm using inverse calculations. Eastern-European Journal of Enterprise Technologies, 5 (4 (101)), 18–25. doi: https://doi.org/10.15587/1729-4061.2019.180993
  20. Shen, B., Shen, Y., Ji, W. (2019). Profit optimization in service-oriented data market: A Stackelberg game approach. Future Generation Computer Systems, 95, 17–25. doi: https://doi.org/10.1016/j.future.2018.12.072

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Published

2022-01-19

How to Cite

Gribanova, E. (2022). Construction of algorithms for solving the inverse problem when using indicators in several calculation functions. Eastern-European Journal of Enterprise Technologies, 1(4 (115), 44–50. https://doi.org/10.15587/1729-4061.2022.251545

Issue

Section

Mathematics and Cybernetics - applied aspects