In recent years, emergency last-mile logistics (ELML) have played an essential role in urban emergencies. The efficient allocation of couriers in ELML is of practical significance to ensure the supply of essential materials, especially in public health emergencies (PHEs). However, couriers allocation becomes challenging due to the instability of demand, dynamic supply comprehension, and the evolutional delivery environment for ELML caused by PHEs. While existing work has delved into couriers allocation, the impact of PHEs on demand-supply-delivery has yet to be considered. In this work, we design PTOCA, a Predict-Then-Optimize Couriers Allocation framework. Specifically, in the prediction stage, we design a resource-aware prediction module that performs spatio-temporal modeling of unstable demand characteristics using a variational graph GRU encoder and builds a task-resource regressor to predict demand accurately. In the optimization stage, firstly, the priority ranking module solves the matching of delivery resources under demand-supply imbalance. Then the multi-factor task allocation module is used to model the dynamic evolutional environment and reasonably assign the delivery tasks of couriers. We evaluate PTOCA using real-world data covering 170 delivery zones, more than 10,000 couriers, and 100 million delivery tasks. The data is collected from JD Logistics, one of the largest logistics service companies. Extensive experimental results show that our method outperforms the baseline in task delivery rate and on-time delivery rate.

With the growth of the depth of neural networks and the scale of data, the difficulty of network training also increases. When the GPU memory is insufficient, it is challenging to train deeper models. Recent research uses tensor swapping and recomputation techniques in a combined manner to optimize memory usage. However, complex dependencies and enormous scales of the DNN graph limit the improvement of single GPU memory optimization. Improper swap and recomputation decisions even bring negative effects on training performance. In this article, we propose a novel hybrid tensor re-generation strategy, called STR, which combines swap and recomputation techniques to find the optimal execution plan for the DNN training when the memory is limited. We formalize our memory optimization problem with constraints that describe the dependency of the operator calculation and the bandwidth usage of the swap. A host checkpoint mechanism is designed to make full use of the swapped tensors, which reduces the cost of the recomputation. We also present a recursive source tracing algorithm to improve the optimization efficiency by constraint relaxation with a performance bound. To optimize large models, we further introduce an approximation method based on a weighted graph coarsening. We implement a prototype of STR as a plugin on TensorFlow and evaluated based on 5 popular DNN models. The experimental result shows that the approximate solution of STR improves the training throughput of ResNet series of models by up to 28.1% compared to the state-of-the-art hybrid optimization strategy.

Integrated Warehousing and Distribution Supply Networks (IWDSN) have shown their high efficiency in E-commerce. Efficient supply capacity prediction is crucial for logistics systems to maintain the delivery capacity to meet users' requirements. However, unforeseen events such as extreme weather and public health emergencies pose challenges in supply forecasting. Previous work mainly infers supply optimization based on the invariant topology of logistic networks, neglecting dynamic routing and distinct node effects reacting to emergencies. To address these challenges, the hierarchical relations among warehouses, sorting centers, and delivery stations in logistic networks are necessary to learn the diverse reactions. In this paper, we propose a hierarchical spatio-temporal graph learning model to predict the emergency supply capacity of IWDSN based on micro and macro graphs. The micro graph shows transportation connectivity while the macro graph shows the geographical correlation. Specifically, it consists of three components. (1) For micro graphs, a metapath aggregation strategy is designed to capture dynamic routing information on both route-view and event-view graphs. (2) For macro graphs, a bipartite graph learning approach to extract spatial representations. (3) For spatio-temporal feature fusion, the spatio-temporal joint forecasting module combines the temporal feature from the time-series encoder with hierarchical spatial features to predict the future supply capacity. The extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model, which achieves state-of-the-art performance compared with advanced baselines.