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Identifying the center of innovation industrial clusters to provide the federal district's evolution foresight

Vol. 26, Iss. 8, AUGUST 2020

Received: 8 June 2020

Received in revised form: 22 June 2020

Accepted: 6 July 2020

Available online: 28 August 2020

Subject Heading: INVESTING

JEL Classification: C63, E17, O21, O36

Pages: 1747–1766

https://doi.org/10.24891/fc.26.8.1747

Yashin S.N. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
jashinsn@yandex.ru

https://orcid.org/0000-0002-7182-2808

Trifonov Yu.V. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
kei@ef.unn.ru

https://orcid.org/0000-0002-4745-0004

Koshelev E.V. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
ekoshelev@yandex.ru

https://orcid.org/0000-0001-5290-7913

Subject. This article deals with the issues of placing and locating the information and logistics center of the Federal District's clusters.
Objectives. The article aims to create a technology to identify the center of innovative industrial clusters, which would ensure their effective economic, financial, information, and logistics cooperation.
Methods. To solve the problem of location of the information and logistics center of clusters in order to anticipate the evolution of the Federal District, we used simulation modeling algorithms, genetic algorithm, and the simulated annealing and pattern search methods.
Results. These methods have been tested for the Volga (Privolzhsky) Federal District of the Russian Federation. As a result of analysis, modeling and calculations, it turns out that the information and logistics center of the Volga (Privolzhsky) Federal District should be the City of Kazan, the Republic of Tatarstan.
Conclusions and Relevance. The deployment of the information and logistics center for the Volga (Privolzhsky) Federal District in Kazan can significantly reduce the transaction costs associated with the regulation of information and transportation flows within the Federal District under study. This will reduce the financial costs in the District and increase the synergistic effect of a large innovation system uniting innovation and industrial clusters in a large area of the entire Federal District. The additional synergies will help the governmental structures and their experts conduct a better policy of economic and financial foresight of the evolution of the Volga (Privolzhsky) Federal District.

Keywords: location problem, genetic algorithm, simulated annealing, pattern search

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