AI Techniques Aid for Optimizing the Collection System of Industrial Plastic Waste

Instead of statistical approaches, artificial intelligence (AI) techniques have been utilized for waste management in many fields owing to their higher accuracy. It provides opportunities to make accurate future predictions of collection demands and detect the optimal collection routes. This study aims to address plastic waste management using AI by applying predicted individual collection demands of industrial plastic waste (IPW) to an integrated collection system, as demonstrated in the Fukuoka Prefecture, Japan. We propose an AI-based approach for applying known collection demands of IPW regarding vehicle routing problems to better integrate the existing IPW collection system. After providing details on future prediction of the collection demands through the machine learning approach, the Euclidean-distance-optimized vehicle routing problem was solved using Python. To further validate this method, an optimal route was estimated for a real road network. Finally, reductions in traveling distance and carbon dioxide (CO2) emissions were evaluated for the collection system both before and after AI-assisted integration. In this study, a distance-optimized collection route was identified, thus demonstrating the feasibility of integrating existing collection systems using AI technology. This integration was proven to be beneficial in terms of the traveling distance (22 km reduced per collection, i.e., 14.2% of the total distance was reduced) and CO2 emissions (4.8 kg-CO2 reduced per collection, i.e., 10.1% of the total emissions were reduced).