An AI-Enabled Smart Reverse Logistics Framework for Sustainable E-Waste Collection and Route Optimization

作者：S. Ananth, M. Nithiya, K. Karthikeyan, K. Vasanth, D. SalmanKhan, M. Jeevaa · 发表于：International Journal of Drug Delivery Technology · 年份：2026 · DOI：10.25258/ijddt.16.35s.24

E-waste has emerged as a serious environmental concern in India, with residential areas having no convenient means of recycling and current recycling systems being oriented towards organizations or involving inconvenient transport for drop-off. This study aims to introduce an AI-Driven Smart E-Waste Collection and Logistics Optimization System, which combines the fields of Artificial Intelligence, Web Technologies, and Sustainable Computing to fill this gap. Decision Tree, Random Forest, and Logistic Regression Machine Learning models are used to classify e-waste, optimize pickup routes through smart route planning, and improve recycling efficiency through AI-assisted sorting at recycling plants. The web-based system allows for convenient doorstep pickup scheduling, real-time tracking, reward point accrual per kilogram of recycled material, and connectivity to recycled product marketplaces. Blockchain technology is used to provide trust through transparent transaction history, reward point verification, and tamper-proof sustainability metrics tracking. Through gamification of participation and removal of transport-related barriers, the system increases residential participation rates while lowering carbon emissions from unorganized e-waste disposal. This integrated approach promotes environmental sustainability, encourages circular economy practices, and fills regulatory funding gaps through the strategic integration of innovative technologies rather than subsidies.