Improving Last-Mile Delivery in E-Commerce Through AI-Powered Route Optimization and Resource Allocation

Abstract

The explosive growth of e-commerce has heightened the importance of last-mile delivery, the final stage in the logistics process where goods are transported to the end consumer. However, last-mile delivery remains one of the most challenging and resource-intensive segments of the supply chain, plagued by inefficiencies such as high costs, delays, and environmental concerns. Artificial Intelligence (AI) has emerged as a transformative technology for addressing these challenges, offering solutions for route optimization, resource allocation, and dynamic decision-making. This paper explores the integration of AI in enhancing last-mile delivery processes by focusing on real-time route optimization and efficient resource utilization. Through advanced algorithms, machine learning, and predictive analytics, AI enables dynamic routing that adapts to changing traffic patterns, weather conditions, and delivery constraints, ensuring timely and cost-effective deliveries. Furthermore, AI-driven systems optimize resource allocation, such as vehicle scheduling, load balancing, and workforce management, to enhance operational efficiency. This research highlights key AI techniques, including reinforcement learning, metaheuristic optimization, and neural network-based forecasting, as pivotal tools in achieving these outcomes. The paper reviews recent advancements in AI technologies applied to last-mile delivery, emphasizing their impact on reducing delivery times, cutting operational costs, and minimizing environmental footprints. Additionally, we examine the challenges of deploying AI in last-mile logistics, including data privacy, integration with legacy systems, and ethical considerations. Case studies from leading e-commerce companies illustrate practical applications and tangible benefits. By analyzing the intersection of AI and logistics, this research provides actionable insights for e-commerce stakeholders aiming to leverage AI for competitive advantage. The findings underscore the potential of AI to revolutionize last-mile delivery and contribute to the development of sustainable, customer-centric logistics ecosystems. Future directions for AI in logistics are also outlined, emphasizing the need for continuous innovation and collaboration between academia and industry.