Simulation Model of a Single-Industry Town and Optimization of Employment Structure Using Agent-Based Approach and Particle Swarm Optimization Algorithm

Currently, there are approximately 320 single-industry towns in Russia. According to estimates from the Fund for the Development of Single-Industry Towns, a nonprofit organization established by Vnesheconombank in October 2014, many of these towns are at risk due to an insufficiently balanced economic structure, characterized by a dominant single industry. Simultaneously, diversifying the economic structures of single-industry cities is hindered by the inertia of labor resources and the need for significant investments in training and retraining staff. To address these challenges and facilitate strategic decision-making, a simulation model for a single-industry town was developed using an agent-based approach and particle swarm optimization. This model optimizes key parameters such as workplaces creation intensities across economic sectors and the proportion of investments for workforce training, significantly enhancing the viability of single-industry towns. The study examines the impact of the characteristics of single-industry towns and the dynamics of their Gross Urban Product (GUP) on the local economy. The model considers two types of actors: local actors focused on high-technology sectors and external actors focused on low-technology industries. Using the proposed agent-based model and particle swarm optimization algorithm, experiments were conducted using artificial data, and the potential for industry diversification and employment structure optimization for a single-industry city was demonstrated.