Optimal Control Model for Territory with Special (Preferential) Development Regime

Authors

  • О. М. Shatalova Institute of Economics of the Ural Branch of the Russian Academy of Sciences (Udmurt Branch), Kalashnikov Izhevsk State Technical University
  • S. A. Likhopud Kalashnikov Izhevsk State Technical University (Izhevsk), JSC «Bystrinskaya Mining Company» (Petropavlovsk-Kamchatsky)

DOI:

https://doi.org/10.22213/2410-9304-2023-2-152-163

Keywords:

strategic prediction, preferential regimes, multicriteria optimization, optimal control, mathematical modeling

Abstract

The article presents the result of multicriteria optimization mathematical model to control territories with a preferential development regime (TPDR) development. TPDR are an institutional form of management of the spatial economy. TPDR should contribute to the outpacing social and economic growth of the regions. Implementing such functions, an effective methodological apparatus for decision-making support is needed. In particular, mathematical methods of optimal control can be used. The purpose of the research was to develop a complex model that allows to make an indicative predictive estimation of the main parameters of the TPDR, as well as multi-criteria optimization. The model is designed to solve the problem of choosing an acceptable control strategy from a discrete set of feasible alternatives. The research used two groups of methodological foundations: deterministic methods of predictive estimation of parameters, as well as methods of vector multicriteria optimization using a generalized criterion. The article provides an example of a numerical implementation of the TPDR control model, which was developed as a part of the study. This example illustrates the practical applicability of the model and the result of multi-objective optimization. The optimization problem was solved from the position of restrained pessimism of the decision maker based on the generalized maximin criterion, the minimax regret criterion, and the optimism-pessimism criterion. The proposed mathematical model of multicriteria optimization provides a comprehensive representation of the main economic factors of the optimal control of the TPRR and contributes the formation of a set of acceptable alternatives, as well as system analysis from the standpoint of rational management. Also, the model reveals the structural and functional content of the TPDR management mechanism and explicates the composition of significant economic parameters. This determines the importance of the model as the basis for the development of an information-analytical system for the optimal control of TPDR.

Author Biographies

О. М. Shatalova, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (Udmurt Branch), Kalashnikov Izhevsk State Technical University

DSc in Economics

S. A. Likhopud, Kalashnikov Izhevsk State Technical University (Izhevsk), JSC «Bystrinskaya Mining Company» (Petropavlovsk-Kamchatsky)

Post-graduate

References

Розен В. В., Смирнова Д. С. Модели многокритериальной оптимизации по качественным критериям // Изв. Сарат. ун-та. Сер. Математика. Механика. Информатика. 2013. Т. 13, вып. 2, ч. 2. С. 37-44.

Надежность и эффективность в технике: справочник: в 10 т. / ред. совет: В. С. Авдуевский (пред.) и др. М.: Машиностроение, 1988. (В пер.). Т. 3. Эффективность технических систем / под общ. ред. В. Ф. Уткина, Ю. В. Крючкова. 328 с.

Норткотт Д. Принятие инвестиционных решений. М.: Банки и биржи; ЮНИТИ, 1997. 348 с.

Гитман Л. Дж., Майкл Д. Джон. Основы инвестирования / пер. с англ. М.: Дело, 1997. 1008 с.

Розен В. В. Математические модели многокритериальной оптимизации по качественным критериям // Компьютерные науки и информационные технологии: материалы междунар. науч. конф. Саратов: Наука, 2012. С. 266-268.

Старовойтов В. В., Голуб Ю. И. Нормализация данных в машинном обучении // Информатика. 2021. Т. 18, № 3. С. 83-96. DOI: doi.org/10.37661/ 1816-0301-2021-18-3-83-96.

Reza Z.F., Maryam S., Nasrin A. Multiple criteria facility location problems: A survey, Applied Mathematical Modelling, 2010, Vol. 34 (7), pp. 1689-1709. https://doi.org/10.1016/j.apm.2009.10.005.

Sanjoy K. P., Priyabrata C., Kamrul A., Syed M. Ali, Golam K.An advanced decision-making model for evaluating manufacturing plant locations using fuzzy inference system, Expert Systems with Applications, 2022, Vol. 191, 116378, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.116378.

Yan, M. R.; Pong, C. S.; Lo, W. Utility-based multicriteria model for evaluating BOT projects, Technological and Economic Development of Economy,2011, 17(2), 207-218. https://doi.org/10.3846/20294913.2011.580585.

Venkata Rao, R. Evaluating flexible manufacturing systems using a combined multiple attribute decision making method.Int. J. Prod. Res., 2008, 46, 1975-1989. https://doi.org/10.1080/00207540601011519.

Wątróbski, J.; Jankowski, J. Guideline for MCDA method selection in production management area.In New Frontiers in Information and Production Systems Modelling and Analysis; Springer: Berlin/Heidelberg, Germany, 2016, pp. 119-138. https://doi.org/10.1007/978-3-319-23338-3_6.

Spronk, J., Steuer, R.E., Zopounidis, C. Multicriteria Decision Aid/Analysis in Finance. In: Multiple Criteria Decision Analysis: State of the Art Surveys.International Series in Operations Research & Management Science, 2005, vol 78.Springer, New York, NY. https://doi.org/10.1007/0-387-23081-5_20.

Wu, H.-Y.; Tzeng, G.-H.; Chen, Y.-H.A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard, Expert Systems with Applications,2009, 36(6), 10135-10147. https://doi.org/10.1016/j.eswa.2009.01.005.

de Almeida, A.T. Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method.Comput.Oper. Res., 2007, 34, 3569-3574. https://doi.org/10.1016/j.cor.2006.01.003.

Azadnia, A.H.; Saman, M.Z.M.; Wong, K.Y. Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process.Int. J. Prod. Res., 2015, 53, 383-408. https://doi.org/10.1080/00207543.2014.935827.

Sałabun W, Wątróbski J, Shekhovtsov A. Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods.Symmetry, 2020, 12(9), 1549. https://doi.org/10.3390/sym12091549.

Ногин В. Д. Принятие решений в многокритериальной среде: количественный подход. М.: ФИЗМАТЛИТ, 2002. 144 с. ISBN 5-9221-0274-5.

Тененев В. А. Решение задачи многокритериальной оптимизации генетическими алгоритмами // Интеллектуальные системы в производстве. 2006. № 2 (8). С. 103-109.

Solomon, E. M.Types of R&D investment and firm productivity: UK evidence on heterogeneity and complementarity in rates of return. Economics of Innovation and New Technology, 2021, 30(5), 536-563. https://doi.org/10.1080/10438599.2020.1846249.

Seidel, T., & von Ehrlich, M.The persistent effects of placed-based policy-Evidence from the West-German Zonenrandgebiet.55th Congress of the European Regional Science Association (conference paper). 2015. http://hdl.handle.net/10419/124779.

Criscuolo, C., Martin, R., Overman, H. G., & Van Reenen, J.Some causal effects of an industrial policy. American Economic Review, 2019, 109(1), 48-85. https://doi.org/10.1257/aer.20160034.

Cerulli, G., Corsino, M., Gabriele, R., &Giunta, A.A dose-response evaluation of a regional R&D subsidies policy. EconomicsofInnovation and NewTechnology, 2022, 31(3), 173-190. https://doi.org/10.1080/10438599.2020.1792604.

Lenihan, H.,McGuirk, H., Kevin M. Driving innovation: Public policy and human capital.Research Policy,2019, 48 (9), 103791.https://doi.org/10.1016/j.respol.2019.04.015.

Lenihan, H., Mulligan, K., Doran, J. et al. R&D grants and R&D tax credits to foreign-owned subsidiaries: Does supporting multinational enterprises R&D pay off in terms of firm performance improvements for the host economy?.J Technol Transf. 2023. https://doi.org/10.1007/s10961-023-09995-9.

Анализ практики применения преференциальных режимов, действующих на территории РФ, с точки зрения их влияния на экономический рост и соответствия заявленным целям: отчет / Д. А. Зайцев; Счетная палата РФ. 2020. URL: https://ach.gov.ru/upload/iblock/d22/d22daa028b1854b51b99c9d2927c2e06.pdf. (дата обращения: 01.02.2023).

Швецов А. Н. Инструменты политики поляризованного пространственного развития // Федерализм. 2018. № 1 (89). С. 82-103.

Шаталова О. М. Об организационно-экономическом механизме инновационного научно-технологического центра как полюса роста и устойчивого развития региональной экономики // Вестник Удмуртского университета. Экономика и право. 2021. Т. 31, № 4. С. 610-620. DOI 10.35634/2412-9593-2021-31-4-610-620.

Кузнецова О. В. География особых экономических зон и их аналогов в России // Региональные исследования. 2020. № 4. С. 19-31. DOI: 10.5922/ 1994-5280-2020-4-2.

World Investment Report 2019.Special Economic Zones.UNCTAD, Geneva, 2019. 237 p.

Boudeville J.-R. L'espace et les pôles de croissance. P., 1968.

Perroux F. Les investissements multinationaux et l'analyse des poles de developpement et des poles d'integration. Revue Tiers-Monde, 1968, vol. 9, no. 34, pp. 239-265.

Published

30.06.2023

How to Cite

Shatalova О. М., & Likhopud С. А. (2023). Optimal Control Model for Territory with Special (Preferential) Development Regime. Intellekt. Sist. Proizv., 21(2), 152–163. https://doi.org/10.22213/2410-9304-2023-2-152-163

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Articles