Dynamic Routing in Urban Logistics: A Comprehensive Review of AI, Real-Time Data, and Sustainability Impacts
DOI:
https://doi.org/10.61194/sijl.v3i2.741Keywords:
Dynamic Routing Algorithms, Urban Logistics, Operational Efficiency, Sustainability, Real-Time Data, Artificial Intelligence, Optimization TechniquesAbstract
This paper examines the impact of dynamic routing algorithms on urban logistics, focusing on their role in improving operational efficiency and environmental sustainability. With the rise of e-commerce and the increasing complexity of urban transport networks, dynamic routing has emerged as a critical solution for reducing delivery times, optimizing fleet usage, and minimizing emissions. The methodology for this review involved a comprehensive search of key academic databases, including Scopus, IEEE Xplore, and Google Scholar, using relevant keywords and inclusion criteria. The results demonstrate that algorithms integrating real-time data, artificial intelligence, and hybrid optimization models significantly enhance routing decisions. Furthermore, real-time systems that incorporate GIS and IoT data enable more responsive and context-aware logistics operations. However, challenges such as infrastructure disparities, data interoperability, and policy support must be addressed to fully realize the potential of dynamic routing in urban environments. The study concludes by emphasizing the importance of policy interventions and collaborative efforts to overcome these barriers and proposes future research directions to improve scalability and data integration in dynamic routing systems.
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