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

In the context of increasing environmental challenges and the demand for sustainable development, traditional resource scheduling models in business management often fail to balance economic efficiency with environmental constraints. To address this gap, this study proposes an enhanced Particle Swarm Optimization (PSO) algorithm, termed OBLPSO, which integrates Opposition-Based Learning (OBL) and a perturbation mechanism. First, OBL generates a high-quality initial population to improve solution diversity, while a cosine curve adaptive strategy dynamically adjusts inertia weights to balance global exploration and local exploitation. Additionally, a perturbation mechanism expands the search range, preventing premature convergence. A multi-objective optimization model is established, incorporating task time, economic cost, and environmental impact (e.g., energy consumption and pollutant emissions) to maximize resource utilization and minimize ecological harm. Experimental results demonstrate that OBLPSO reduces task processing time by 29.7% and energy consumption by 16.1% compared to benchmark algorithms (e.g., ACO, GA, and standard PSO) under large-scale tasks (2000 tasks). The proposed method provides a robust solution for sustainable resource scheduling in an enterprise management environment with economic constraints.