MODELLING THE STATE OF CLUSTER STRUCTURES USING QUEUING SYSTEM MODELS
Abstract
Modeling the state of cluster structures using queuing system models is a vital tool for analyzing their efficiency and forecasting development. Cluster structures, as dynamic systems, consist of enterprises, research institutions, government bodies, and other participants interacting within a shared economic space. Queuing system models enable consideration of the specific features of these interactions, including resource flows, innovation exchange, and information distribution. This article explores approaches to using such models to evaluate cluster workload, productivity, and identify bottlenecks that may limit their development. Special attention is given to analyzing parameters influencing the state of cluster structures, including the intensity of incoming requests, processing times, and interaction levels between participants. Queuing system methods allow the modeling of cluster development scenarios under various conditions, such as increased infrastructure load or growing competition. The article also examines the impact of innovative technologies, particularly digital platforms, on optimizing cluster operations and enhancing participant coordination. The proposed modeling approach provides deeper insights into the functioning of cluster structures, aiding in informed managerial decision-making. Practical application of the study's findings allows for process optimization within clusters, improving their efficiency and competitiveness. The use of queuing system models is a promising research direction in regional economics, as it facilitates the development of sustainable mechanisms for cluster growth in the dynamic conditions of modern markets.
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