Smart Fleet Management for Urban Sustainability: Analysis, Optimization, and Electrification Approach in Narni District's Waste Collection

Waste management poses significant challenges for contemporary cities due to rapid waste growth, high collection costs, limited treatment capacities, and environmental concerns. This paper provides a comprehensive analysis and optimization of the waste collection fleet at ASM Terni a multi-utility company in Italy, with a specific focus on waste bins and internal combustion engine vehicles. The research employs the Geospatial Bin Clustering method in the Narni district as a case study and through segmentation of the district into 12 clusters, the study enhances decision-making for fleet scheduling. In addition, the study integrates the Google Maps API for route planning and the Hungarian algorithm for vehicle-cluster assignment optimization. The research also investigates the potential of electrification as a strategic measure to align with local Clean City initiatives, promote community health, and address carbon footprint concerns. This research provides a novel vehicle-to-cluster assignment to waste fleet optimization, combining geospatial analytics and strategic vehicle assignment. This approach contributes to more efficient waste management practices, aligning with broader sustainability goals and fostering environmentally friendly initiatives in the waste collection process.