Research on Urban Waste Sorting Transportation Path Scheduling Based on Greedy Algorithm and Clustering Optimization

This study investigates the scheduling of urban waste sorting transportation routes using a greedy algorithm and clustering optimisation to achieve a multi-objective balance between transportation efficiency, cost, and carbon emissions reduction. Firstly, a single-vehicle basic route optimisation model is constructed, and a mixed-integer programming model is solved using a greedy algorithm. In a case study involving 30 collection points and a load capacity of 125 tonnes, the optimal driving distance algorithm is obtained, and the route is optimised through clustering and shortest path strategies. Secondly, in a multi-vehicle collaborative scenario, a multi-objective model incorporating carbon costs is constructed, solved using integer linear programming and genetic algorithms, and it is verified that transportation costs are positively correlated with the distance to collection points. Finally, transfer station location constraints are incorporated to construct a ‘location-route-carbon emissions’ model. K-means clustering is used to divide the collection points into three clusters to determine transfer station locations, and the minimum spanning tree is then used to optimise the routes, achieving a reduction in overall costs. The research findings provide decision-making tools for waste sorting transportation, applicable to collection and transportation system planning, and contribute to the achievement of the ‘dual carbon’ goals.