Evolutionary Computing for Supply Chain Optimization: A Comparative Study of Cost, Time, and Inventory Trade-offs

This study explores the application of evolutionary computing techniques to optimize supply chain management by balancing cost, time, and inventory trade-offs. A multi-objective genetic algorithm is implemented to improve operational efficiency, minimize shipping costs, reduce lead times, and maintain optimal stock levels. A synthetic dataset and real-world supply chain data are utilized for training and validation. Results indicate that evolutionary algorithms provide a robust framework for supply chain decision-making, achieving significant cost reductions and time efficiency improvements. The study highlights the effectiveness of these methods in complex logistical scenarios, emphasizing their potential for real-world applications.