Optimization of Solid Waste Collection Routes for Emissions Reduction Using a Hybrid Genetic Algorithm

High population density and heavy traffic congestion in major cities create significant challenges in determining optimal routes for efficient waste collection and minimal environmental impacts. In this paper, we considered a customized Capacity Constrained Vehicle Route Problem (CVRP) variant that reduces CO2 emissions based on the EMFAC CARB model of Indian vehicle emission factors recommended by CPCB. Based on waste bin allocation data of Wards 26, 27, and 30 of Dhanbad and historical traffic data during typical waste collection times, the proposed hybrid genetic algorithm accounts for real-world delays due to traffic congestion. Incorporating traffic-adjusted travel times along with distance considerations and optimizing for emissions-based fitness function led to a $4 \%$ decrease in total emissions while maintaining a comparable total distance to that of the distance-based fitness function. The algorithm was also compared with traditionally used metaheuristics. Further, the optimized routes were visualized on the open street map using Folium. This study showed the potential of Hybrid Genetic Algorithm optimization for municipal solid waste collection in the context of achieving Sustainable Development Goals by including cost-effective and environmentally responsible urban solid waste management practices.