Hou Ying, Guo Xinyu, Han Honggui, Wang Jingjing, Du Yongping
IEEE Trans Cybern. 2024 Nov;54(11):6358-6370. doi: 10.1109/TCYB.2024.3454346. Epub 2024 Oct 30.
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.
蚁群优化（ACO）算法因其分布式计算能力而被广泛应用于即时配送订单调度中。然而，当面对不同的物流状态时，订单配送效率会降低。为了提高ACO的性能，提出了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中提取特征，以描述实时物流状态。从上述三个特征中进一步选取五个关键因素，以辅助问题建模和ACO设计。其次，考虑客户可接受的配送时间，建立了自适应即时配送模型。可接受时间通过事件维度特征中的紧急订单标记和天气状况来计算。第三，提出了一种自适应ACO算法来获取即时配送订单调度。根据提取的关键因素调整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，以验证即时配送模型的有效性。与其他现有最优算法相比，所提出的AACO-RTLF算法在即时配送订单调度中显示出其优势。
