Agent-based modeling and optimization of the characteristics for research-and-production clusters
Abstract
This paper presents a developed agent-based simulation model for the development of research-and-production clusters in Russia implemented with the use of high-tech enterprises located in four science cities (Troitsk, Obninsk, Pushchino and Protvino) as the case study. A new approach to modeling and optimization of gross metropolitan product (GMP) is proposed, taking into account the influence of the “gravity effect” on the redistribution of labor resources between developing science cities and appropriate enterprises united in single research and research-and-production clusters An important element of this approach is the formation of various scenarios for the strategic development of the research-and-production clusters being assessed and support for the possibility of choosing the most preferable scenario using an evolutionary optimization algorithm. An enlarged simulation model has been developed and implemented in AnyLogic describing the possible development trajectories of science cities with a corresponding change in the values of the most important characteristics: the number of economically active population, the number of research-and-production enterprises, the volume of products produced in high-tech sectors of the economy, GMP, etc. The designed framework is intended primarily for the management of research-and-production clusters implementing the strategy of innovative development. Such a framework uses methods of system dynamics and agent-based simulation modeling supported in the AnyLogic system, genetic optimization algorithms and GIS mapping for science cities, etc. to implement the required functionality. The approbation of the framework was completed with the use of real data published in the approved strategies of the relevant science cities development. As a result of the numerical experiments carried out, some recommendations were proposed for the development of the research-and-production clusters under study considering their mutual influence and the existing base of resources.
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