Evaluation of Different Biowaste Collection Scenarios and Comparison of Periodic and Dynamic Collection

Efficient biowaste management is critical for sustainable urban development, and directly influences environmental outcomes and operational efficiency. As dense urban areas face increasing volumes of waste and stricter regulations, as in France’s “loi APEC”, optimizing collection strategies is becoming a pressing challenge. The aim of this paper is to compute routing scenarios in order to determine which one is the more sustainable. The study examines biowaste management in urban environments for one year, evaluating and comparing the economic and environmental impacts of two distinct waste collection strategies: traditional periodic collection and an advanced signal-based collection system. To achieve this, we used a synthetic population model and clustering algorithms to map the distribution of biowaste bins throughout the study area. Subsequently, a Capacity Vehicle Routing Problem (CVRP) algorithm was applied to optimize the collection routes for both scenarios, and we specifically adapted it to represent the specificities of bio-waste management. The traditional method involves weekly collection regardless of bin fill levels, whereas the signal-based approach dynamically triggers collection based on predefined fill thresholds. The signal-based collection system initiates waste collection when a bin exceeds an upper threshold ( t 1 ) and includes all bins filled above a lower threshold ( t 0 , where t 1 ≥ t 0 ). The results demonstrate a 35% reduction in waste collection time in the signal-based system on a running year, highlighting its potential for significant improvements in operational efficiency and environmental sustainability. This paper provides a rigorous analysis of the methodologies used and discusses the implications of transitioning to adaptive biowaste collection systems in urban settings.