Research on Inventory Management of Medical Supplies Based on Hybrid Intelligent Optimization Algorithm

Effective inventory management of medical supplies is vital in healthcare due to fluctuating demand, strict expiration dates, and the need for multi-objective optimization. Traditional methods often struggle with variable demand and complex constraints, resulting in excess inventory or shortages. This article introduces GA-PSO, a hybrid optimization algorithm that combines genetic algorithms with particle swarm optimization. By integrating dynamic demand forecasting and multi-objective optimization, GA-PSO enhances medical supply management. Simulation results indicate that GA-PSO significantly outperforms traditional methods, achieving a 39.3% reduction in costs, a stock-out rate of just 1.3%, and minimal shelf life loss at 0.6%. This study offers an intelligent decision-making tool for medical supply inventory, greatly improving operational efficiency and service quality while providing insights for smart healthcare supply chains.