AI ALGORITHMS FOR OPTIMIZING UNIVERSITY RESOURCE MANAGEMENT
DOI:
https://doi.org/10.59787/2413-5488-2026-55-3-137-147Keywords:
artificial intelligence, optimization algorithms, university resource management, digital transformation of education, genetic algorithms, particle swarm optimization, ant colony optimization.Abstract
This article examines the potential of AI algorithms for optimizing university resource management in the context of the digital transformation of higher education. Modern approaches to the intellectualization of management processes, based on the use of optimization algorithms and machine learning methods, are analyzed. Attention is paid to heuristic and meta-heuristic approaches such as genetic algorithms, particle swarm modeling, and ant colony modeling, which are used to allocate educational, scientific, and human resources at universities. The research methodology includes a combination of complementary scientific methods: analysis of scientific publications and regulations, a comparative analytical approach, algorithmic modeling and optimization methods, statistical data processing, and conceptual modeling. The empirical basis of the study is data from 11 universities located in the cities of Astana and Almaty. The results demonstrate the significant potential of using AI algorithms to improve the efficiency of university resource management. The use of intelligent analysis and optimization methods enables more informed management decisions, promotes the rational allocation of educational, research, and human resources, and improves the quality of educational activities in the context of the digitalization of higher education.
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