Abstract
The subject of this study is to create technologies to identify a center of innovation and industrial clusters, which would provide their effective economic and financial, information and logistics interaction. For this purpose, the paper proposes a method for solving the problem of locating an information and logistics center of clusters in a federal district. This method is based on the use of simulation modeling algorithms. An innovation and industrial cluster center is determined on the basis of minimizing the sum of distances from the planned center to clusters. The maximum remoteness of the center from district nuclear power plants and atom cities is also taken into account. With this aim in view, a genetic algorithm, a simulated annealing method and a pattern search are used.
This approach is tested for the Volga Federal District. As a result, it has been obtained that the information and logistics center of the Volga Federal District should be the city of Kazan.
If the information and logistics center of the Volga Federal District is to be actually located in Kazan, this will considerably reduce transaction costs associated both with regulation of information flows and with traffic streams within the confines of the federal district under investigation. And this, in turn, will lead to a reduction in financial costs in the Volga Federal District and, most importantly, to an increase in the synergy effect of a large innovation system that brings together innovation and industrial clusters on a large territory of the entire federal district.
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Acknowledgments
The research has been performed with financial support from the Russian Foundation for Basic Research within the confines of scientific project No. 19-010-00932 “Creating an innovation system development model for industrial regions under current conditions of social and economic development”.
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Yashin, S.N., Yashina, N.I., Koshelev, E.V., Sukhanov, D.A., Kuznetsov, V.P. (2022). Locating an Information and Logistics Center for Innovation and Industrial Clusters in a Federal District. In: Popkova, E.G. (eds) Imitation Market Modeling in Digital Economy: Game Theoretic Approaches. ISC 2020. Lecture Notes in Networks and Systems, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-030-93244-2_49
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