Department of Industrial Engineering, Faculty of Engineering, Texas A&M University, Kingsville, Texas, United States of America.
Research Article
World Journal of Advanced Research and Reviews, 2023, 19(03), 1719-1722
Received on 19 June 2023; revised on 20 August 2023; accepted on 25 August 2023
In the contemporary landscape of global logistics, the capacity to dynamically optimize delivery routes in real time is a critical determinant of operational efficiency, customer satisfaction, and environmental sustainability. Conventional routing algorithms, while effective in static or semi-dynamic contexts, often fail to adapt to rapid changes in traffic conditions, weather disruptions, road closures, and last-minute delivery requests. This paper proposes a novel Deep Learning-based architecture that leverages Long Short-Term Memory (LSTM) networks for real-time traffic forecasting and Deep Q-Networks (DQNs) for adaptive route decision-making. The system processes live inputs from GPS sensors, weather APIs, and traffic feeds to dynamically generate optimal delivery paths. Extensive simulations using synthetic and real-world datasets from urban logistics providers demonstrate substantial improvements in delivery time (up to 21%), fuel consumption (13%), and vehicle utilization rates (17%) compared to traditional heuristics-based methods. This research provides both theoretical contributions and practical implementation guidelines for logistics operators seeking intelligent, AI-driven solutions for route optimization.
Real-Time Route Optimization; Deep Learning in Logistics; LSTM Traffic Forecasting; Reinforcement Learning (DQN); Urban Delivery Systems; AI-Driven Transportation Planning
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Oluwatumininu Anne Ajayi. Real-Time Route Optimization in Logistics: A Deep Learning Approach. World Journal of Advanced Research and Reviews, 2023, 19(3), 1719-1722. Article DOI: https://doi.org/10.30574/wjarr.2023.19.3.1524