• Tejashree Yelicherla
• Dr Devaraj Verma C
• Sai Ujwal V
• Roopa Nanditha S
• Shriya

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.

Supply Chain Optimization, Evolutionary Computing, Genetic Algorithms, Cost Reduction, Inventory Management

"Evolutionary Computing for Supply Chain Optimization: A Comparative Study of Cost, Time, and Inventory Trade-offs", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b820-b825, May-2025, Available :http://www.jetir.org/papers/JETIR2505199.pdf

"Evolutionary Computing for Supply Chain Optimization: A Comparative Study of Cost, Time, and Inventory Trade-offs", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb820-b825, May-2025, Available at : http://www.jetir.org/papers/JETIR2505199.pdf