Research on Urban Logistics Delivery Route Planning Considering Traffic Congestion

随着城市化进程的加快，城市物流配送面临着日益复杂的路径规划问题。本文基于算术优化算法(Arithmetic Optimization Algorithm, AOA)，提出了一种新颖的城市物流配送路径规划方法——基于维度学习的算术优化算法(Dimension Learning Strategy Arithmetic Optimization Algorithm, DLSAOA)。该方法通过引入维度学习技术，优化了传统算术优化算法在处理高维数据时的效率和准确性。最后通过基准函数测试和城市物流配送路径规划问题实例验证。实验结果表明，所提方法在多个配送场景中均表现出优异的性能，相较于传统算术优化算法，配送成本降低了25.2%，配送成功率提高了30.6%。With the acceleration of urbanization, urban logistics distribution faces increasingly complex path planning problems. This paper proposes a novel urban logistics distribution path planning method based on the Arithmetic Optimization Algorithm (AOA)—the Dimension Learning Strategy Arithmetic Optimization Algorithm (DLSAOA). This method optimizes the efficiency and accuracy of the traditional arithmetic optimization algorithm in handling high-dimensional data by incorporating dimension learning techniques. Finally, the method is validated through benchmark function tests and case studies of urban logistics distribution path planning problems. Experimental results demonstrate that the proposed method exhibits excellent performance across multiple distribution scenarios, achieving a 25.2% reduction in distribution costs and a 30.6% increase in delivery success rates compared to traditional arithmetic optimization algorithms.