Ant colony optimization (ACO) algorithm is widely used in the instant delivery order scheduling because of its distributed computing capability. However, the order delivery efficiency decreases when different logistics statuses are faced. In order to improve the performance of ACO, an adaptive ACO algorithm based on real-time logistics features (AACO-RTLFs) is proposed. First, features are extracted from the event dimension, spatial dimension, and time dimension of the instant delivery to describe the real-time logistics status. Five key factors are further selected from the above three features to assist in problem modeling and ACO designing. Second, an adaptive instant delivery model is built considering the customer’s acceptable delivery time. The acceptable time is calculated by emergency order mark and weather conditions in the event dimension feature. Third, an adaptive ACO algorithm is proposed to obtain the instant delivery order schedules. The parameters of the probability equation in ACO are adjusted according to the extracted key factors. Finally, the Gurobi solver in Python is used to perform numerical experiments on the classical datasets to verify the effectiveness of the instant delivery model. The proposed AACO-RTLF algorithm shows its advantages in instant delivery order scheduling when compared to the other state-of-the-art algorithms.

中文翻译：

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基于实时物流特征的自适应蚁群优化算法，实现即时配送


蚁群优化 （ACO） 算法因其分布式计算能力而被广泛用于即时配送订单调度。但是，当面对不同的物流状态时，订单交付效率会降低。为了提高 ACO 的性能，该文提出一种基于实时物流特征的自适应 ACO 算法 （AACO-RTLFs）。首先，从即时配送的事件维度、空间维度和时间维度提取特征，描述实时物流状态;从上述三个特征中进一步选择了 5 个关键因素，以辅助问题建模和 ACO 设计。其次，考虑到客户可接受的交货时间，构建自适应即时交付模型。可接受时间由事件维度要素中的紧急命令标记和天气条件计算得出。第三，提出一种自适应 ACO 算法来获取即时交货订单时间表。根据提取的关键因子调整 ACO 中概率方程的参数。最后，使用 Python 中的 Gurobi 求解器对经典数据集进行数值实验，以验证即时交付模型的有效性。与其他最先进的算法相比，提出的 AACO-RTLF 算法显示了其在即时交货订单调度方面的优势。