Simulation of migration and demographic processes to support decision-making

Andranik Sumbatovich  Akopov

Andranik Sumbatovich Akopov Central Economics and Mathematics Institute of the Russian Academy of Sciences, Moscow, Russia; MIREA – Russian Technological University, Moscow, Russia https://orcid.org/0000-0003-0627-3037

Keywords: agent-based modeling, migration and demographic processes, population forecasting, territorial population concentration, particle swarm optimization, labor market, regions of Russia

Abstract

Currently, many enterprises in Russia are facing a shortage of labor resources. Unequal spatial development negatively affects the production characteristics of companies. In particular, declining birth rates and increasing migration from peripheral to central and European regions of the country significantly affect the labor market. Therefore, this article proposes a new approach to predicting and optimizing migration and demographic processes by using agent-based models and a hybrid particle swarm optimization algorithm. The goal of this approach is to find solutions that will contribute to implementing a balanced spatial development strategy and population growth in regions. In order to address this problem, a hybrid multi-swarm particle swarm optimization algorithm (R-HMSPSO) has been proposed. This approach aims to maximize the population size, while considering strategies for uniform population growth across regions and constraints on territorial population concentration measures (TPC). The search for optimal solutions using R-HMSPSO is based on a set of decision variables that determine the rate of wage growth, creation of workplaces, housing construction, and the pace of social infrastructure development. All these factors affect the quality of life and the demographic situation in regions. The TRC measure serves as primary constraints when solving the maximization problem for population size. The improved economic situation in a region affects its attractiveness, leading to a redistribution of migration flows and a change in population concentration. Therefore, an evolutionary search for optimal variable values is applied simultaneously across all regions. This model was aggregated through objective functions with an R-HMSPO algorithm. As a result of optimization experiments, the most preferable solutions for improving the demographic situation in Russia’s regions have been identified. These solutions contribute to both population growth and more balanced spatial development.

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