International Journal of Science, Engineering and TechnologyAuthors: Dr. Pankaj Malik, Apaar Mishra, Mohit Goyal, Mohit Bajpai, Mohid Sheikh
Abstract: The rapid expansion of e-commerce and on-demand delivery services has created a need for intelligent logistics systems capable of adapting to dynamic environments. This research presents a Real-Time Delivery Optimization framework that integrates Internet of Things (IoT) technology with Deep Learning techniques to improve delivery efficiency and operational performance. IoT devices, including GPS trackers, RFID tags, vehicle sensors, and weather monitoring systems, continuously collect real-time data related to vehicle location, traffic conditions, fuel consumption, and delivery status. The collected data are processed using Long Short-Term Memory (LSTM) networks for accurate delivery time prediction and Deep Reinforcement Learning (DRL) algorithms for dynamic route optimization. The proposed system enables real-time decision-making by continuously updating delivery routes based on changing traffic and environmental conditions. Experimental evaluation was conducted using logistics datasets containing GPS traces, traffic information, weather records, and delivery history. Results demonstrated that the proposed framework achieved 95.2% ETA prediction accuracy, reduced average delivery time by 32%, improved route efficiency by 21%, and decreased fuel consumption by 18% compared to conventional routing methods. Furthermore, customer satisfaction increased by approximately 17% due to improved delivery reliability and timely updates. These findings indicate that the integration of IoT and Deep Learning provides an effective solution for intelligent logistics management and real-time delivery optimization in modern transportation networks.
DOI: http://doi.org/10.5281/zenodo.20802364
