Design and real-time scheduling of multi-objective logistics routing optimization model based on graph attention network

Abstract With the booming logistics industry, multi-objective logistics transportation accounting for more than 60% and the annual logistics transportation volume growth rate of about 12%, traditional logistics path optimization algorithms are difficult to cope with complex and dynamic actual logistics scenarios. This paper proposes a multi-objective logistics path optimization model based on graph attention network and realizes real-time scheduling. By constructing the logistics network as a graph structure, using the graph attention mechanism to capture the complex relationship between nodes, designing innovative multi-objective fusion strategies and real-time information embedding mechanisms, the model is iteratively updated and its adaptability is enhanced. Experimental results show that in small-scale logistics networks, compared with traditional algorithms such as genetic algorithms, this model can reduce transportation costs by more than 20%; shorten transportation time by more than 25%; have the lowest cargo loss rate; and have the highest customer satisfaction. In scenarios such as large-scale logistics networks and complex road conditions, it also shows significant advantages, and the real-time scheduling response time is greatly shortened. For example, in the dynamic traffic change scenario in Case 1, the path adjustment can be completed in only 5 min, which is much faster than other comparison models. This study provides efficient operation solutions for logistics companies and promotes the development of the logistics industry towards intelligence and efficiency.