An AI-Driven Framework Combining K-Means Clustering and VRP Optimization for Sustainable Waste Collection

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Urban waste management in developing countries faces persistent challenges, including inefficient routing, high operational costs, and significant environmental impact. This study presents a hybrid framework that integrates K-means clustering with Capacitated Vehicle Routing Problem (CVRP) optimization to improve municipal waste collection efficiency within a reverse logistics perspective. Applied to the Technical Landfill Center (CET) of Guelma, Algeria, the model groups 23 urban sectors into operationally coherent clusters before optimizing collection routes. The results show a 63.6% reduction in fleet size, a 69.14% decrease in daily travel distance, and estimated annual CO2 savings of 381 metric tons, while maintaining full-service coverage. Built entirely on open-source tools, the proposed framework offers a computationally efficient and interpretable optimization approach, providing a scalable decision-support tool for sustainable city logistics in resource-constrained settings.

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