基于区域的训练方法用于最后一公里路由：使用图神经网络和指针网络
A zone-based training approach for last-mile routing using Graph Neural Networks and Pointer Networks
AI总结：
本文提出基于区域的训练方法，利用图神经网络和指针网络优化最后一公里路由，通过生成地理区域减少配送时间。
摘要：
快速发展的电子商务增长已将最后一公里配送网络推向极限，其中小的路由收益转化为更低的成本、更快的服务和更少的排放。经典启发式方法在旅行时间高度不对称（例如单行道、拥堵）时难以适应。本文提出了一种基于深度学习的最后一公里路由方法，以生成由停止序列组成的地理区域，以最小化最后一公里配送时间。所提出的方法是一种编码器-解码器架构。每条路线表示为一个完整的有向图，其节点是停靠点，边权重是不对称的旅行时间。图神经网络编码器生成节点嵌入，捕捉停靠点之间的空间关系。指针网络解码器随后利用嵌入和路线的起始节点，依次选择下一个停靠点，为每个未访问的节点分配作为下一个目的地的概率。离散全球网格系统中的单元格，其包含训练数据中的路线停靠点，被获取并聚类以生成大小相似的地理区域，在其中训练和推理过程被分开。随后，每个区域训练一个不同的模型实例，仅考虑包含在该区域中的训练路线的停靠点。该方法使用2021年亚马逊最后一公里路由挑战中的洛杉矶路线进行评估。通用训练和基于区域训练的结果进行了比较，显示基于区域的训练在平均预测路线长度上比通用训练有所减少。基于区域的方法的性能改进随着每条路线的停靠点数量增加而更加显著。
英文摘要：
Rapid e-commerce growth has pushed last-mile delivery networks to their limits, where small routing gains translate into lower costs, faster service, and fewer emissions. Classical heuristics struggle to adapt when travel times are highly asymmetric (e.g., one-way streets, congestion). A deep learning-based approach to the last-mile routing problem is presented to generate geographical zones composed of stop sequences to minimize last-mile delivery times. The presented approach is an encoder-decoder architecture. Each route is represented as a complete directed graph whose nodes are stops and whose edge weights are asymmetric travel times. A Graph Neural Network encoder produces node embeddings that captures the spatial relationships between stops. A Pointer Network decoder then takes the embeddings and the route's start node to sequentially select the next stops, assigning a probability to each unvisited node as the next destination. Cells of a Discrete Global Grid System which contain route stops in the training data are obtained and clustered to generate geographical zones of similar size in which the process of training and inference are divided. Subsequently, a different instance of the model is trained per zone only considering the stops of the training routes which are included in that zone. This approach is evaluated using the Los Angeles routes from the 2021 Amazon Last Mile Routing Challenge. Results from general and zone-based training are compared, showing a reduction in the average predicted route length in the zone-based training compared to the general training. The performance improvement of the zone-based approach becomes more pronounced as the number of stops per route increases.
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