Garbage Collection and Transportation Path Optimization Using Deep Learning

The planning of urban waste collection routes is a core issue in the logistics management of smart cities. Its goal is to achieve efficient waste collection at the lowest operational cost while ensuring that all waste collection points receive timely and effective service. To address the limitations of traditional heuristic algorithms in terms of solving efficiency and optimization quality, this paper proposes a deep learning-based method for waste collection route planning. This method employs a graph attention network architecture, using a multi-head attention mechanism to effectively capture the spatial relationships and constraints between waste collection points. It combines the self-regression decoding structure of the pointer network to generate the optimal route sequence. Experimental results show that this method performs well in optimizing waste collection routes, effectively reducing collection costs and improving route planning efficiency. Compared to traditional algorithms, it shows improvements in both solution quality and computational speed.