GreenRoute Ballari: A Hybrid Approach for Optimizing Waste Collection Routes Using K-Means Clustering and Genetic Algorithm

Abstract

The exponential growth of the population in Ballari has exacerbated the challenge of municipal waste management, necessitating the development of efficient waste collection strategies to address environmental and social concerns. In response to that our system introduces a hybrid approach that employs K-means clustering and Genetic Algorithm, for optimizing waste collection routes tailored to the unique waste generation profiles of the region. By utilizing Geographic Information System (GIS) data that is in the form of GeoJSON files containing the spatial distribution of waste collection points, we propose a methodology that combines K-means clustering and Genetic Algorithm to develop intelligent and fuel-efficient routes. Our system not only benefits municipal authorities in resource deployment and planning but also aids waste carrier vehicle drivers in prioritizing routes. Our findings contribute to the transformation of Ballari City towards eco-friendly and smart urban development