Providing accurate estimated time of package delivery on users' purchasing
pages for e-commerce platforms is of great importance to their purchasing
decisions and post-purchase experiences. Although this problem shares some
common issues with the conventional estimated time of arrival (ETA), it is more
challenging with the following aspects: 1) Inductive inference. Models are
required to predict ETA for orders with unseen retailers and addresses; 2)
High-order interaction of order semantic information. Apart from the
spatio-temporal features, the estimated time also varies greatly with other
factors, such as the packaging efficiency of retailers, as well as the
high-order interaction of these factors. In this paper, we propose an inductive
graph transformer (IGT) that leverages raw feature information and structural
graph data to estimate package delivery time. Different from previous graph
transformer architectures, IGT adopts a decoupled pipeline and trains
transformer as a regression function that can capture the multiplex information
from both raw feature and dense embeddings encoded by a graph neural network
(GNN). In addition, we further simplify the GNN structure by removing its
non-linear activation and the learnable linear transformation matrix. The
reduced parameter search space and linear information propagation in the
simplified GNN enable the IGT to be applied in large-scale industrial
scenarios. Experiments on real-world logistics datasets show that our proposed
model can significantly outperform the state-of-the-art methods on estimation
of delivery time. The source code is available at:
this https URL

本文提出一种基于归纳图转换器（IGT）的回归函数方法，通过原始特征信息与结构图数据进行参数训练，以预估出物流包裹的交付时间，实验结果表明该方法可以显著提高交付时间的估计精度。

基于归纳图变换的送货时间估计模型

Inductive Graph Transformer for Delivery Time Estimation

Unmanned aerial vehicles (UAVs), also known as drones, have emerged as a
promising mode of fast, energy-efficient, and cost-effective package delivery.
A considerable number of works have studied different aspects of drone package
delivery service by a supplier, one of which is delivery planning. However,
existing works addressing the planning issues consider a simple case of perfect
delivery without service interruption, e.g., due to accident which is common
and realistic. Therefore, this paper introduces the joint ground and aerial
delivery service optimization and planning (GADOP) framework. The framework
explicitly incorporates uncertainty of drone package delivery, i.e., takeoff
and breakdown conditions. The GADOP framework aims to minimize the total
delivery cost given practical constraints, e.g., traveling distance limit.
Specifically, we formulate the GADOP framework as a three-stage stochastic
integer programming model. To deal with the high complexity issue of the
problem, a decomposition method is adopted. Then, the performance of the GADOP
framework is evaluated by using two data sets including Solomon benchmark suite
and the real data from one of the Singapore logistics companies. The
performance evaluation clearly shows that the GADOP framework can achieve
significantly lower total payment than that of the baseline methods which do
not take uncertainty into account.

本文介绍了关于联合地面和空中交付服务优化和规划的框架，旨在最小化总交付成本并考虑到起飞和故障条件的不确定性。采用三阶段随机整数规划模型，并运用分解方法，以两个数据集进行性能评估，表明该框架比不考虑不确定性的基线方法具有明显的降低总支付的优势。

联合地面和空中包裹投递服务：基于随机优化方法的研究

Joint Ground and Aerial Package Delivery Services: A Stochastic  Optimization Approach

Beyond visual line-of-sight connectivity is key for use cases of unmanned
aerial vehicles (UAVs) such as package delivery, infrastructure inspection, and
rescue missions. Cellular networks stand ready to support flying UAVs by
providing wide-area, quality, and secure connectivity for UAV operations.
Ensuring reliable connections in the presence of UAV movements is important for
safety control and operations of UAVs. With increasing height above the ground,
the radio environment changes. Using terrestrial cellular networks to provide
connectivity to the UAVs moving in the sky may face new challenges. In this
article, we share some of our findings in mobility support for cellular
connected UAVs. We first identify how the radio environment changes with
altitude and analyze the corresponding implications on mobility performance. We
then present evaluation results to shed light on the mobility performance of
cellular connected UAVs. We also discuss potential enhancements for improving
mobility performance in the sky.

文章阐述了无人机与蜂窝网络间的行动支持及电台环境的影响，并探讨了提高其在天空中运行性能的潜在方法