Economic optimization of business administration resources: Multi-objective scheduling method based on improved PSO.

This article focuses on improving business management resource scheduling by proposing an enhanced Particle Swarm Optimization (PSO) algorithm called OBLPSO, which integrates Opposition-Based Learning (OBL) and a perturbation mechanism to address premature convergence and enhance search capabilities. The study establishes a multi-objective optimization model that simultaneously considers task time, economic cost, and environmental impact to maximize resource utilization while minimizing ecological harm. Experimental results using real enterprise data demonstrate that OBLPSO outperforms benchmark algorithms such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and standard PSO by reducing task processing time by 29.7% and energy consumption by 16.1% in large-scale scheduling scenarios. The proposed method offers a robust and efficient solution for sustainable resource scheduling in business administration under environmental and economic constraints.