Dynamic Routing Optimization in Logistics Using Machine Learning: Towards Efficient and Sustainable Supply Chains

Abstract

Efficient and sustainable supply chain management is crucial for modern businesses to remain competitive in dynamic market landscapes. This paper proposes a novel approach to address these challenges through dynamic routing optimization using machine learning techniques. By harnessing the power of data analytics, predictive modeling, and optimization algorithms, this framework aims to enhance the efficiency and sustainability of logistics operations. This article discusses the integration of machine learning into traditional routing optimization strategies, enabling real-time adaptation to changing demand patterns, traffic conditions, and environmental factors. Furthermore, it highlights the potential benefits of our approach, including reduced transportation costs, minimized carbon emissions, and improved service levels. Through case studies and simulations, it demonstrates the effectiveness of our methodology in optimizing routes, improving resource utilization, and mitigating environmental impact. Ultimately, this research contributes to the advancement of intelligent supply chain management practices, paving the way toward a more resilient, efficient, and sustainable future.