Hybrid Optimization Software with Big Data

This article presents the development of a hybrid optimization software designed for inventory management in supermarkets, leveraging Big Data and advanced algorithms. The system integrates Artificial Neural Networks (ANN), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) to address critical variables such as current stock, monthly demand, unit cost, and sales price. By combining predictive analytics with optimization techniques, the software reduces costs, enhances decision-making accuracy, and prevents overstocking or stockouts. The results demonstrate significant improvements in efficiency, offering a robust and adaptive solution for dynamic market conditions and promoting sustainability in inventory management.