Navigating Global Trade: Genetic Algorithm Approaches to E-Commerce Supply Chain and Inventory Optimization

This study explores the application of genetic algorithms (GAs) for optimizing supply chain and inventory control in e-commerce companies engaged in international trade. Amidst the complexities of global economic interactions and the rise of e-commerce, effective inventory management becomes pivotal for operational efficiency and competitive advantage. The research adopts ABC stock taxonomy to categorize e-commerce products, allowing for nuanced inventory strategies based on product type. A mathematical model centered on Class A inventory is developed and solved using genetic algorithms, demonstrating the model’s suitability for real-time inventory adjustments and cost optimization. The research methodology involves experimental simulations with a population size of 500 iterations over an inventory cycle of 365 days. The proposed scheme outlined in this paper achieves an 11.48% reduction in overall costs after optimization and management while concurrently decreasing inventory levels and minimizing conflicts arising from production and business operations. This study contributes significantly to supply chain management and e-commerce, proposing a novel approach that integrates genetic algorithms for inventory optimization. This research aligns with the growing need for sophisticated, technology-driven solutions in managing international e-commerce supply chains. It offers practical insights and a robust framework for e-commerce companies to optimize their inventory in response to dynamic market demands, enhancing their operational efficiency and competitiveness in the global market.