In last-mile delivery, drivers frequently deviate from planned delivery routes because of their tacit knowledge of the road and curbside infrastructure, customer availability, and other characteristics of the respective service areas. Hence, the actual stop sequences chosen by an experienced human driver may be potentially preferable to the theoretical shortest-distance routing under real-life operational conditions. Thus, being able to predict the actual stop sequence that a human driver would follow can help to improve route planning in last-mile delivery. This paper proposes a pair-wise attention-based pointer neural network for this prediction task using drivers’ historical delivery trajectory data. In addition to the commonly used encoder–decoder architecture for sequence-to-sequence prediction, we propose a new attention mechanism based on an alternative specific neural network to capture the local pair-wise information for each pair of stops. To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost. Results from an extensive case study on real operational data from Amazon’s last-mile delivery operations in the US show that our proposed method can significantly outperform traditional optimization-based approaches and other machine learning methods (such as the Long Short-Term Memory encoder–decoder and the original pointer network) in finding stop sequences that are closer to high-quality routes executed by experienced drivers in the field. Compared to benchmark models, the proposed model can increase the average prediction accuracy of the first four stops from around 0.229 to 0.312, and reduce the disparity between the predicted route and the actual route by around 15%.

在最后一英里交付中，司机经常偏离计划的交付路线，因为他们对道路和路边基础设施、客户可用性以及各自服务区域的其他特征有默契的了解。因此，在实际操作条件下，有经验的人类司机选择的实际停车顺序可能比理论上的最短距离路线更可取。因此，能够预测人类驾驶员将遵循的实际停车顺序有助于改进最后一英里交付中的路线规划。本文针对此预测任务提出了一种基于注意力的成对指针神经网络，该网络使用驾驶员的历史交付轨迹数据。除了用于序列到序列预测的常用编码器-解码器架构外，我们提出了一种基于替代特定神经网络的新注意机制，以捕获每对停靠点的局部成对信息。为了进一步捕获路线的全局效率，我们提出了一种新的迭代序列生成算法，该算法在模型训练后用于识别产生最低运营成本的路线的第一站。来自亚马逊在美国的最后一英里交付业务的真实运营数据的广泛案例研究结果表明，我们提出的方法可以显着优于传统的基于优化的方法和其他机器学习方法（例如长短期记忆编码器 - 解码器和原始指针网络）寻找更接近该领域经验丰富的驾驶员执行的高质量路线的停止序列。与基准模型相比，