AI-BASED ROUTE OPTIMIZATION FOR SUSTAINABLE LOGISTICS

Abstract

The rapid expansion of global logistics and transportation networks has led to a substantial increase in fuel consumption, greenhouse gas emissions, and overall operational costs, posing significant environmental and economic challenges. As a result, sustainable logistics has become a critical area of focus, demanding intelligent and adaptive solutions that can effectively balance operational efficiency with environmental responsibility. This study presents an advanced Artificial Intelligence (AI)-based route optimization framework designed to enhance logistics performance while minimizing environmental impact. The proposed framework integrates machine learning algorithms, real-time data analytics, and predictive modeling techniques to optimize delivery routes dynamically. It incorporates multiple real-world factors, including traffic congestion patterns, weather conditions, road constraints, and vehicle capacity limitations, to generate efficient and eco-friendly routing decisions. Furthermore, reinforcement learning mechanisms enable the system to continuously improve its performance by learning from historical and real-time data. To evaluate the effectiveness of the proposed approach, extensive simulations were conducted using synthetic dataset based on real-world traffic patterns and standard logistics benchmarks. The experimental results demonstrate that the AI-driven model achieves a reduction in fuel consumption of up to 25%, decreases delivery delays by approximately 30%, and significantly lowers carbon emissions compared to conventional routing and heuristic-based methods. The findings of this research underscore the transformative potential of AI in enabling sustainable logistics practices. By improving route efficiency and reducing environmental impact, the proposed framework contributes to the development of greener supply chains and supports global efforts toward reducing carbon footprints in the transportation sector. Future work will explore integration with smart city infrastructure, electric vehicle systems, and Internet of Things (IoT)-enabled logistics platforms.