Subject. This article deals with modeling of R&D costs optimal cross-financing within the regions of Russia that have the appropriate scientific potential. Objectives. The article aims to develop a model for optimizing and planning R&D costs cross-financing in the Federal District, which takes into account the specific technological and economic results of research in the District regions. Methods. Various R&D costs by type of work are made dependent on three areas of planning of the District regions' innovative development, namely, investment, production, and financial. All the three processes are considered simultaneously. Nonlinear regressions of R&D costs by type of work get optimized through a genetic algorithm, simulated annealing, and pattern search, which helps calculate the reserve or lack of corresponding R&D costs in each region of the Federal District. Results. The article presents an author-developed model with certain positive characteristics to optimize and plan R&D costs cross-financing in the Federal District. Conclusions and Relevance. The results of global optimization allow us to conclude that in the conditions of saving federal budget funds, the Federal District can partially finance all the R&D costs in those regions that need it. In order to identify such regions in a more substantiated way, it is necessary to analyze this situation in detail, that is, in the context of various research costs by type of work. The presented approach can facilitate the adoption of better decisions by government entities and their experts in relation to the planning of innovative development of industrial regions of the country.
Keywords: innovative development of regions, investment planning, production planning, financial planning, research and development costs
References:
Kruschwitz L., Lorenz D. Investitionsrechnung. De Gruyter Oldenbourg, 2019, 427 s. URL: Link
Dehmer S.P., Pardey P.G., Beddow J.M., Chai Y. Reshuffling the Global R&D Deck, 1980–2050. PLoS ONE, 2019, vol. 14, iss. 3. URL: Link
Kiselakova D., Sofrankova B., Cabinova V. et al. The Impact of R&D Expenditure on the Development of Global Competitiveness within the CEE EU Countries. Journal of Competitiveness, 2018, vol. 10, no. 3, pp. 34–50. URL: Link
Feoktistova O.A. [Planning of the research costs: The project-based approach]. Finansovyi zhurnal = Financial Journal, 2014, no. 1, pp. 69–80. URL: Link (In Russ.)
Gaponenko V.F. [The questions of costs’ planning on implementation of the scientific research works in the system of the Interior Ministry of Russia]. Trudy Akademii upravleniya MVD Rossii = Journal of Proceedings of Academy of Management of the Ministry of Internal Affairs of Russia, 2018, no. 1, pp. 58–62. URL: Link (In Russ.)
Yashin S., Koshelev E., Yashina N. et al. Foresight of Volga Federal District Innovation System Development using a Multi-Objective Genetic Algorithm. International Journal of Technology, 2020, vol. 11, iss. 6, pp. 1171–1180. URL: Link
Salimi N., Rezaei J. Evaluating Firms’ R&D Performance Using Best Worst Method. Evaluation and Program Planning, 2018, vol. 66, pp. 147–155. URL: Link
Bin A., Azevedo A., Duarte L. et al. R&D and Innovation Project Selection: Can Optimization Methods be Adequate? Procedia Computer Science, 2015, vol. 55, pp. 613–621. URL: Link
Huang M.C., Liou M.-H., Iwaki Y. The Impact of R&D and Innovation on Global Supply Chain Transition: GTAP Analysis on Japan’s Public R&D Investment. Journal of Social and Economic Development, 2021, vol. 23, pp. 447–467. URL: Link
Sadollah A., Nasir M., Geem Z.W. Sustainability and Optimization: From Conceptual Fundamentals to Applications. Sustainability, 2020, vol. 12, iss. 5, pp. 2–34. URL: Link
Kalyanmoy D. Multiobjective Optimization Using Evolutionary Algorithms. New York, John Wiley & Sons, Inc., 2001, 518 p.
Lopatin A.S. [Simulated annealing]. Stokhasticheskaya optimizatsiya v informatike, 2005, vol. 1, pp. 133–149. URL: Link (In Russ.)
Ingber L., Rosen B. Genetic Algorithms and Very Fast Simulated Reannealing: A Comparison. Mathematical and Computer Modelling, 1992, vol. 16, iss. 11, pp. 87–100. URL: Link90108-W
Conn A.R., Gould N.I.M., Toint Ph.L. A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds. SIAM Journal on Numerical Analysis, 1991, vol. 28, iss. 2, pp. 545–572. URL: Link
Conn A.R., Gould N.I.M., Toint Ph.L. A Globally Convergent Augmented Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints and Simple Bounds. Mathematics of Computation, 1997, vol. 66, pp. 261–288. URL: Link
Babynin M.S., Zhadan V.G. [A primal interior point method for the linear semidefinite programming problem]. Zhurnal vychislitel'noi matematiki i matematicheskoi fiziki = Computational Mathematics and Mathematical Physics, 2008, vol. 48, no. 10, pp. 1746–1767. URL: Link (In Russ.)