An optimized clustering method for the vehicle routing problem with time windows to improve Moroccan waste management

Inefficient waste collection and transportation in Morocco have led to growing operational and environmental concerns, highlighting the importance of optimizing their routing to mitigate resources and carbon emissions. In this study, the waste collection process is modeled as a Vehicle Routing Problem with Time Windows (VRPTW), where multiple vehicles are responsible for collecting waste from distributed bins and delivering it to a central recycling facility, all while adhering to time window and vehicle capacity constraints. To address this problem, we developed a two-phase algorithm named Clustered Modified Large Neighborhood Search (CMLNS). It starts with the clustering phase, where the k-means algorithm is employed to partition the problem into clusters, each represented as a Traveling Salesman Problem (TSP). Next, we individually route each cluster using diverse insertion strategies. To further improve the solution, we propose a Modified Large Neighborhood Search (MLNS), which partially destroys the solution and strategically reconstructs it using diverse insertion operators in each iteration. We conducted numerical experiments using the classic Solomon benchmark instances to evaluate the performance of the proposed algorithm. Our results indicate that the CMLNS algorithm outperforms several state-of-the-art algorithms, and it achieved new best-known solutions in two instances. These improvements demonstrate the algorithm’s effectiveness in reducing transportation costs and improving logistical efficiency in Moroccan waste management systems.