Metaheuristic logistic optimization in education &science sector of modern economics in complex & dynamic terms

Abstract

This study examines metaheuristic logistic optimization in the education and R&D sectors under uncertainty and dynamic complexity. Traditional optimization methods often fail in such conditions, while metaheuristic algorithms (genetic algorithms, particle swarm optimization, simulated annealing, and hybrid approaches) offer flexible tools for solving multidimensional and multi-criteria problems. Their adaptability and scalability enable efficient resource allocation, scheduling, project prioritization, and information flow management. The integration of metaheuristics supports intelligent management systems, improves decision-making, and enhances efficiency in rapidly changing environments.