Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which arise as different batches of goods arrive sequentially, causing performance drops. We argue that the short-term lookahead information available in modern logistics systems is key to mitigating this issue, especially during distribution shifts. We formulate online 3D-BP with lookahead parcels as a Model Predictive Control (MPC) problem and adapt the Monte Carlo Tree Search (MCTS) framework to solve it. Our framework employs a dynamic exploration prior that automatically balances a learned RL policy and a robust random policy based on the lookahead characteristics. Additionally, we design an auxiliary reward to penalize long-term spatial waste from individual placements. Extensive experiments on real-world datasets show that our method consistently outperforms state-of-the-art baselines, achieving over 10% gains under distributional shifts, 4% average improvement in online deployment, and up to more than 8% in the best case--demonstrating the effectiveness of our framework.

The rapid growth of online shopping has intensified competition among logistics companies, highlighting the importance of customer expansion, i.e., identifying customers willing to establish long-term contracts. Although existing approaches frame customer expansion as a node classification task using heterogeneous graph learning to capture complex interactions between a customer and other items, it is computationally infeasible to utilize all neighboring interactions on large-scale logistics graphs. Current sub-sampling methods reduce computational load by sampling a small part of neighborhood for training. However, they introduce substantial information loss, particularly affecting high-degree nodes and decreasing predictive accuracy. To address this, we introduce NeighSqueeze, a novel approach that groups structurally and semantically similar nodes, substantially reducing the neighbors count and facilitating full-neighbor learning. NeighSqueeze consists of three modules designed to efficiently and effectively enable node grouping on billion-scale heterogeneous graphs: (1) Structure-tightness-based neighbor filtering reduces the high redundancy and complexity in similarity computations. (2) Hybrid similarity graph construction addresses the difficulty of measuring node similarity at scale; and (3) A two-level grouping strategy resolves the label dominance issue within groups. We evaluate NeighSqueeze on JD Logistics, one of the largest logistics companies in China. Compared with sub-sampling methods, our NeighSqueeze exhibits lower runtime and memory usage with full-neighbor training on the compressed graph, while simultaneously improving average precision over 28.9% in offline evaluation and increase new customer exploration rate by 18.6% in online A/B testing.

In last-mile logistics, couriers are typically assigned fixed zones to perform door-to-door deliveries. In practice, packages in some delivery zones might not be fulfilled on time due to couriers taking irregular leave for sickness or higher-priority task assignments, e.g., services for VIPs and regulatory training. Beyond the costly real-world practice, i.e., hiring temporary workers, analysis of historical data reveals that a daily delivery task rescheduling among on-duty couriers can be a cost-effective and efficient alternative. It involves individual workload assessments and delivery task assignments, both of which existing methods can not address adequately: (i) Existing courier workload assessment methods are not tailored for downstream optimization tasks, leading to poor performance. (ii) Efficiency-oriented task assignment methods may lead to unfair workload among the couriers. To address the above two limitations, in this paper, we propose D3-TR, a data-driven method for task reassignment among present couriers. Firstly, we design a consistency-guided predictor that can quickly and precisely predict the workload of couriers. Secondly, based on this predictor, we design a workload-aware genetic algorithm to solve the optimal task allocation problem. Experimental results underscore the superiority of our method over several baselines. Furthermore, real-world deployment on millions of orders demonstrates the effectiveness of our solution, yielding an average of 3.9% improvement in the on-time delivery rate.

Customer expansion, i.e., growing a business's existing customer base by acquiring new customers, is critical for scaling operations and sustaining the long-term profitability of logistics companies. Although state-of-the-art works model this task as a single-node classification problem under a heterogeneous graph learning framework and achieve good performance, they struggle with extremely positive label sparsity issues in our scenario. Multi-task learning (MTL) offers a promising solution by introducing a correlated, label-rich task to enhance the label-sparse task prediction through knowledge sharing. However, existing MTL methods result in performance degradation because they fail to discriminate task-shared and task-specific structural patterns across tasks. This issue arises from their limited consideration of the inherently complex structure learning process of heterogeneous graph neural networks, which involves the multi-layer aggregation of multi-type relations. To address the challenge, we propose a Structure-Aware Hierarchical Information Sharing Framework (SrucHIS), which explicitly regulates structural information sharing across tasks in logistics customer expansion. SrucHIS breaks down the structure learning phase into multiple stages and introduces sharing mechanisms at each stage, effectively mitigating the influence of task-specific structural patterns during each stage. We evaluate StrucHIS on both private and public datasets, achieving a 51.41% average precision improvement on the private dataset and a 10.52% macro F1 gain on the public dataset. StrucHIS is further deployed at one of the largest logistics companies in China and demonstrates a 41.67% improvement in the success contract-signing rate over existing strategies, generating over 453K new orders within just two months.

An emerging type of fraud involves malicious senders exploiting the blind shipment and cash-on-delivery (COD) mechanisms by dispatching large volumes of unsolicited, low-cost parcels. If unsuspecting receivers accept these parcels, they pay for both shipping and goods; otherwise, logistics providers bear the round-trip shipping costs. Existing detection techniques, which rely on extensive labeled cases, struggle with this emerging fraud because receivers' unawareness and low transaction values discourage complaints, resulting in few confirmed cases. Therefore, we propose leveraging receivers' complaints, though not initially collected for fraud detection, to uncover subtle indicators of fraud patterns, while addressing three challenges: (C1) noise-rich dialogues(C2) data privacy concerns, and (C3) ever-evolving fraud patterns. To address them, we design BLOFF, a Blind shipment detection Framework for LO gistics Fraud powered by large language models (LLMs). Specifically, BLOFF includes three components: i) Sensitivity Anonymization to protect sensitive user information; ii) Dialogue Profile Distillation to transform informal dialogues into structured representation, addressing C1, and distill knowledge from a teacher LLM (GPT-4o) to a lightweight student LLM (ChatGLM4-9B), addressing C2; ii) Multi-faceted Context Augmentation to enhance the interpretation of fraud signatures and adaptation of evolving patterns, addressing C3. We evaluate BLOFF on about 56,000 complaints records collected from JD Logistics between January and November 2024. Results show that BLOFF outperforms state-of-the-art methods, achieving a 10.19% improvement in precision. Furthermore, during its real-world deployment in December 2024, BLOFF identified over 90 fraudulent parcels with a 91.4% precision.

In logistic systems, demand prediction is an essential task providing the basis for improving the quality of terminal services, such as pick-up and delivery efficiency. However, the geographical scope of operations across multiple cities brings challenges due to the sparsity of user behavior data, hindering accurate predictions. Despite cross-city prediction methods potentially solving this problem by relying on the label of overlapping users in different cities, annotating these overlapping users is expensive. Additionally, the dynamic and diverse nature of user behaviors complicates feature transfer between cities. In this work, we define the logistics demand prediction problem as forecasting pick-up and delivery demand for zones, the smallest operational units in logistics systems, in different cities. To address the challenge, we propose TSTL, a Transferable Spatio-Temporal Learning framework for cross-city logistics prediction with sparse user data. TSTL advances existing methods from two aspects: (1) User-level invariant representation module extracts consistent user representations for overlapping and non-overlapping users across cities. (2) User-zone graph aggregation module enhances user embeddings by integrating dynamic interactions, such as logistics behaviors, into inherent user relations. Finally, the multi-city transfer module fine-tunes model parameters for city-invariant knowledge adoption and predicts future logistics demand. We implement and evaluate TSTL on one of the largest logistics systems. Extensive offline experiments and real-world deployment demonstrate the effectiveness of TSTL.

Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies that fail to pinpoint a location, have led to significant costs. Address rewriting has emerged as a solution to rectify these abnormal addresses. Despite the critical need, existing address rewriting methods are limited, typically tailored to correct specific error types, or frequently require retraining to process new address data effectively. In this study, we introduce AddrLLM, an innovative framework for address rewriting that is built upon a retrieval augmented large language model. AddrLLM overcomes aforementioned limitations through a meticulously designed Supervised Fine-Tuning module, an Address-centric Retrieval Augmented Generation module and a Bias-free Objective Alignment module. To the best of our knowledge, this study pioneers the application of LLM-based address rewriting approach to solve the issue of abnormal addresses. Through comprehensive offline testing with real-world data on a national scale and subsequent online deployment, AddrLLM has demonstrated superior performance in integration with existing logistics system. It has significantly decreased the rate of parcel re-routing by approximately 43%, underscoring its exceptional efficacy in real-world applications.

An efficient last-mile delivery scheme in logistics benefits customers, couriers, and the platform. In practice, the delivery scope of a delivery station is divided into multiple areas, each of which is covered by a courier. The long distances between the delivery station and areas limit the couriers' delivery efficiency given that they need to travel back and forth multiple times a day. To solve this problem, we explore an inter-trip cooperation scheme for last-mile delivery, in which couriers traveling to the delivery station and back to corresponding areas earlier can help to take others' orders back. Coordinating the courier cooperation is challenging because we need to consider the courier's status, e.g., locations, and vehicle capacity constraint simultaneously. In this work, we design an inter-trip cooperation-based last-mile delivery system, InCo, aiming to minimize the average order delivery time. InCo includes two components: i) a time-aware spanning tree algorithm to generate the cooperation result for a group of couriers; and ii) a capacity-constrained courier grouping algorithm to optimize the courier grouping result iteratively. Extensive evaluation results with real-world order data collected from one of the largest logistics companies show that InCo improves the average saved delivery time and reduces average travel time by up to 80.2% and 28.4%, respectively, compared to baseline methods. The deployment results show InCo improves the average courier working efficiency by 21.6% to the state-of-the-practice.

Human behavior prediction is an essential AI-based task, which has inspired many real-world applications. In last-mile logistics, predicting couriers' behavior can benefit the couriers' preference learning and workflow optimization. In this paper, we devote to the behavioral prediction of courier workload and quantify their workload by the working time spent at each area of interest (AOI). Considering the behavior interpretability of inverse reinforcement learning (IRL), existing studies have applied IRL to some real-world transportation prediction scenarios. However, in last-mile logistics, the platform assigns multiple orders to each courier, and couriers also receive new tasks in real-time, which additionally influence the couriers' subsequent decisions. The uncertainty in decision spaces and dynamic the workflow distribution make it more challenging to predict the couriers' working time. In this paper, we propose CourIRL, a practical IRL-based framework leveraging cross-attention to integrate Couriers' historical and spatio-temporal features to predict their future working time. CourIRL formulates the couriers' pick-up and delivery tour as a sequential decision-making process and designs a model-free IRL to learn decision-making preference vectors. A multi-head cross-attention mechanism-based deep regression model is proposed for fine-grained working-time prediction. The results of extensive experiments on two real-world datasets demonstrate that the proposed CourIRL surpasses the state-of-the-art baselines by an average of 6.11% across settings, showing the efficacy and potential contributions of CourIRL in last-mile logistics.

Accurate prediction of order transportation time is essential for customer satisfaction in logistics. Existing methods based on origin-destination (OD) pairs do not consider the diversity of road segments, while route-based methods may fail to account for real-time traffic conditions due to the infrequent dispatch schedules of logistics vehicles. In reality, e-commerce platforms have collaborated with multiple logistics companies for parcel delivery, providing a richer dataset that offers a more comprehensive view of real-time transportation conditions. The key insight is that data from one company can serve as internal capability detectors and data from others can act as external environment detectors. However, a significant challenge arises in inferring travel-time-correlated station pairs across different companies, especially without full disclosure of station information. To address this, we design an Adaptive cross-platform Transportation time prediction framework built upon a hypergraph structure, named AdaTrans, comprising: i) A spatial-temporal routing graph learner employs node-centric and edge-centric hyperedges to address the complex, non-pairwise correlations among stations and station pairs within and across companies; ii) A spatial-temporal graph-based transportation time predictor that utilizes multi-task learning to enhance overall transportation time prediction by leveraging the correlations between interconnected sub-tasks (i.e., dwell and travel times prediction) Extensive evaluation with real-world data collected from JD.com, a leading e-commerce platform in China, demonstrates that consolidating records from other companies reduces RMSE, MAE, and MAPE by 12.63%, 5.18%, and 16.67%, compared to state-of-the-art methods.

Accurately estimating package delivery time is essential to the logistics industry, which enables reasonable work allocation and on-time service guarantee. This becomes even more necessary in mixed logistics scenarios where couriers handle a high volume of delivery and a smaller number of pickup simultaneously. However, most of the related works treat the pickup and delivery patterns on couriers' decision behavior equally, neglecting that the pickup has a greater impact on couriers' decision-making compared to the delivery due to its tighter time constraints. In such context, we have three main challenges: 1) multiple spatiotemporal factors are intricately interconnected, significantly affecting couriers' delivery behavior; 2) pickups have stricter time requirements but are limited in number, making it challenging to model their effects on couriers' delivery process; 3) couriers' spatial mobility patterns are critical determinants of their delivery behavior, but have been insufficiently explored. To deal with these, we propose TransPDT, a Transformer-based multi-task package delivery time prediction model. We first employ the Transformer encoder architecture to capture the spatio-temporal dependencies of couriers' historical travel routes and pending package sets. Then we design the pattern memory to learn the patterns of pickup in the imbalanced dataset via attention mechanism. We also set the route prediction as an auxiliary task of delivery time prediction, and incorporate the prior courier spatial movement regularities in prediction. Extensive experiments on real industry-scale datasets demonstrate the superiority of our method. A system based on TransPDT is deployed internally in JD Logistics to track more than 2000 couriers handling hundreds of thousands of packages per day in Beijing, and the average daily delivery timely rate of deployed stations is 0.68% higher than the non-deployed stations.

Recently, integrated warehouse and distribution logistics systems are widely used in E-commerce industries to adjust to constantly changing customer demands. It makes the prediction of purchase demand and delivery supply capacity a crucial problem to streamline operations and improve efficiency. The interaction between such demand and supply not only relies on their economic relationships but also on consumer psychology caused by daily events, such as epidemics, promotions, and festivals. Although existing studies have made great efforts in the joint prediction of demand and supply considering modeling the demand-supply interactions, they seldom refer to the impacts of diverse events. In this work, we propose MulSTE, a Multi-view Spatio-Temporal learning framework with heterogeneous Event fusion. Firstly, an Event Fusion Representation (EFR) module is designed to fuse the textual, numerical, and categorical heterogeneous information for emergent and periodic events. Secondly, a Multi-graph Adaptive Convolution Recurrent Network (MGACRN) is developed as the spatio-temporal encoder (ST-Encoder) to capture the evolutional features of demand, supply, and events. Thirdly, the Event Gated Demand-Supply Interaction Attention (EGIA) module is designed to model the demand-supply interactions during events. The evaluations are conducted on two real-world datasets collected from JD Logistics and public websites. The experimental results show that our method outperforms state-of-the-art baselines in various metrics.

Empowered by the widespread adoption of Internet of Things (IoT) devices and smartphones, last-mile delivery services have evolved to accommodate both delivery and pickup tasks. An essential challenge in last-mile delivery is efficiently planning routes for couriers to handle pre-scheduled delivery requests as well as stochastic pickup requests. Existing work approaches this problem by either adjusting routes on the fly when new requests arise or preplanning routes based on predicted future pickup requests. However, these methods either compromise the optimality of planned routes or heavily rely on the accuracy of predictions. In this work, we take conformal prediction as an opportunity to address the issue of prediction uncertainty. We design ROPU, a novel courier route planning framework for logistics systems that incorporates conformal prediction into reinforcement learning. Our work advances the existing work from two aspects: (i) Pickup request prediction utilizes spatial-temporal conformal prediction to capture historical pickup request patterns, providing a unified spatial-temporal conformal interval with high confidence (ii) A spatial-temporal attention network assesses location importance from various perspectives and enables the actor to perceive time and integrate the spatial-temporal conformal interval. We implement and evaluate ROPU on one of the largest logistics platforms. Extensive experiment results demonstrate that our method outperforms other state-of-the-art methods with improvements of at least 30.49% in the pickup overdue rate, 25.00% in the delivery overdue rate, and 5.49% in the traveling distance metric.

Node ranking in heterogeneous graphs, which quantifies the relative importance of nodes, can often be improved by incorporating information from relevant paths. Graph database and heterogeneous graph neural network (HGNN) are two main approaches to better solve this problem. Graph databases support efficient path queries for flexible path types but require manual design to combine results for node ranking. Conversely, current HGNNs can automatically integrate semantic information from multiple linear path types for accurate node ranking. However, our experiments show that they fail to outperform a multi-layer perceptron model that utilizes features extracted from multiple nonlinear conditional paths, which can be handled by graph databases. Therefore, we aim to enable HGNN to take advantage of these path types for better performance. However, HGNNs require a generalized path schema to define the structure of input paths, and incorporating each additional path type will significantly increase the required system memory and sampling time for HGNNs. To address these limitations, we introduce CompNode, a novel framework based on a new unified path schema definition called Complex-path, which is used to describe all the required path types, including nonlinear conditional path types. Then, we design a pre-aggregation method to reduce the required system memory and sampling time by pre-aggregating the same type of complex-path. Furthermore, we develop a model that combines semantic information from all aggregated complex-paths for accurate node ranking. Real-world experiments on identifying top potential high-value customers show CompNode outperforms state-of-the-art HGNNs by 20% in average precision and the previously deployed graph database method by 252% in success rate.

In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus ensuring employee productivity and customer satisfaction. Currently, few related works focus on couriers’ level delivery timely rate prediction, and there are complex spatial correlations between couriers and road districts in the express scenario, which makes traditional real-time prediction approaches hard to utilize. To deal with this, we propose a deep spatial-temporal neural network, RCCNet to model spatial-temporal correlations. Specifically, we adopt Node2vec, which can encode the road network-based graph directly to capture spatial correlations between road districts. Further, we calculate couriers’ historical time-series similarity to build a graph and employ graph convolutional networks to capture the correlation between couriers. We also leverage historical sequential information with long short-term memory networks. We conduct experiments with real-world express datasets. Compared with other competitive baseline methods widely used in industry, the experiment results demonstrate its superior performance over multiple baselines.

Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers’ fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD Logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec .

Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services such as Google Maps. However, this is typically not practical for some location-based service providers due to real-world challenges like commercial competition and high costs (recurring fees). In this paper, we design a new cost-effective Geocoding framework to automatically infer the geographic coordinates from textual addresses. To achieve this, we take the E-Commerce logistics service as a concrete scenario and designCoMiner, an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data.CoMinerincludes three main components, (1) A POI-level clustering model, (2) A Delivery Mobility Graph (DMG), and (3) A behavior-driven address ranking model. Furthermore, we designCoMiner-W, a coordinates mining algorithm based on WiFi data, to further enhance the effectiveness ofCoMiner. We conduct extensive experiments on three large-scale datasets whereCoMineroutperforms the state-of-the-art methods by 20.3%. Moreover, we have designed an abnormal delivery event detection system based onCoMinerand deployed it at JD Logistics, which brings a significant reduction in abnormal delivery event rates.

Fine-grained community-building information, such as building names and accurate geographical coordinates, is critical for a range of practical applications like navigation and door-to-door services (e.g., on-demand delivery and last-mile delivery). A common practice of traditional methods to gather community-building information usually relies on manual collection, which is typically labor-intensive and time-consuming. To address these issues, we utilize the massive data generated from e-commerce delivery services and design a framework, AutoBuild, for fine-grained large-scale community-building labeling. AutoBuild consists of two main components: (i) a Location Candidate Detection Module that identifies potential building names and coordinates from multi-source delivery data, and (ii) a Progressive Building Matching Model that employs trajectory modeling, human behavior analysis, and heterogeneous graph alignment to match building names and coordinates. To evaluate the performance of AutoBuild, we applied it to two real-world multi-modal datasets from Beijing City and Chengdu City. The results reveal that AutoBuild significantly outperforms multiple baseline models by 50-meter accuracy of 81.8% and 100-meter accuracy of 95.9% in Beijing City. More importantly, we conduct a real-world case study to demonstrate the practical impact of AutoBuild in last-mile delivery.

Points of Interest (POIs), such as entertainment, dining, and living, are crucial for urban planning and location-based services. However, the high dynamics and expensive updating costs of POIs pose a key roadblock for their urban applications. This is especially true for developing countries, where active economic activities lead to frequent POI updates (e.g., merchants closing down and new ones opening). Therefore, POI updating, i.e., detecting new POIs and different names of the same POIs (alias) to update the POI database, has become an urgent but challenging problem to address. In this paper, we attempt to answer the research question of how to detect and update large-scale POIs via a low-cost approach. To do so, we propose a novel framework called UrbanPOI, which formulates the POI updating problem as a tagging and detection problem based on multi-modal logistics delivery data. UrbanPOI consists of two key modules: (i) a hierarchical POI candidate generation module based on the POINet model that detects POIs from shipping addresses; and (ii) a new POI detection module based on the Siamese Attention Network that models multi-modal data and crowd intelligence. We evaluate our framework on real-world logistics delivery datasets from two Chinese cities. Extensive results show that our model outperforms state-of-the-art models in Beijing City by 26.2% in precision and 10.7% in F1-score, respectively.

Logistics audience expansion, the process for logistics companies to find potential long-term customers, is one of the most important tasks for business growth. However, existing methods for conventional audience expansion fall short due to two significant challenges, the intricate interplay of multiple complex factors in the logistics scenario and the emphasis on long-term logistics service usage instead of one-time promotions. To address the above limitations, we design LOGAE-TKG, a logistics audience expansion method based on a temporal knowledge graph, which consists of three components: (i) a temporal logistics knowledge graph pre-trained model to model the effect of multiple complex factors and build a solid logistics knowledge base for contracting and usage prediction; (ii) an intention learning model with data augmentation-based comparison to capture the contracting intention; (iii) a future pattern discovery model to uncover post-contract patterns. We evaluate and deploy our method on the JingDong e-commerce platform. Extensive offline experiment results and real-world deployment results demonstrate the effectiveness of our method.

Prediction of couriers' delivery timely rates in advance is essential to the logistics industry, enabling companies to take preemptive measures to ensure the normal operation of delivery services. This becomes even more critical during anomaly conditions like the epidemic outbreak, during which couriers' delivery timely rate will decline markedly and fluctuates significantly. Existing studies pay less attention to the logistics scenario. Moreover, many works focusing on prediction tasks in anomaly scenarios fail to explicitly model abnormal events, e.g., treating external factors equally with other features, resulting in great information loss. Further, since some anomalous events occur infrequently, traditional data-driven methods perform poorly in these scenarios. To deal with them, we propose a deep spatial-temporal attention model, named DeepSTA. To be specific, to avoid information loss, we design an anomaly spatio-temporal learning module that employs a recurrent neural network to model incident information. Additionally, we utilize Node2vec to model correlations between road districts, and adopt graph neural networks and long short-term memory to capture the spatial-temporal dependencies of couriers. To tackle the issue of insufficient training data in abnormal circumstances, we propose an anomaly pattern attention module that adopts a memory network for couriers' anomaly feature patterns storage via attention mechanisms. The experiments on real-world logistics datasets during the COVID-19 outbreak in 2022 show the model outperforms the best baselines by 12.11% in MAE and 13.71% in MSE, demonstrating its superior performance over multiple competitive baselines.

Last-mile delivery from delivery stations to customers’ places is now mainly finished by dedicated couriers. In practice, each courier generally collects orders destined for one delivery area at the delivery station and delivers orders to customers. However, the long distance between the delivery station and the delivery area due to practical reasons, e.g., expensive delivery station rental fee in the downtown area, increases the delivery courier’s travel time and decreases the efficiency of the state-of-the-practice last-mile delivery scheme. In this paper, we solve the problem with relay transportation, where a relay courier collects orders at the delivery station and sends them to delivery couriers, and delivery couriers focus on the order delivery at corresponding delivery areas. We design a real-time relay courier scheduling system called REDE to minimize the average relay order delivery time (ARODT) considering the relay and delivery couriers’ mobility and the order destination distribution. First, a heterogeneous task aware route prediction algorithm is proposed to characterize the delivery courier’s mobility. Then a distance-aware greedy algorithm and an ARODT-constrained exchange algorithm are designed to generate the relay route, which is updated with real-time order pickup requests. Extensive evaluation results with real-world logistics data from 100 delivery stations in 38 cities show that REDE reduces ARODT by up to 8.4% compared to baseline methods. The online A/B tests show that compared to the state-of-the-practice method, REDE improves the delivery courier’s working efficiency and the daily number of pickup orders by 20.13% and 4.51%, respectively.

Crowd logistics, as an emerging delivery paradigm, provides a cost-efficient way of leveraging crowdsourcing couriers to help express enterprises to match the surging delivery demands that are hard to be addressed by regular couriers only during online mega sale days. However, it is a challenging problem how to recruit an appropriate number of crowdsourcing couriers and assign an appropriate number of parcels to them and regular couriers, as many practical issues need to be considered, such as the dynamic competitive crowdsourcing market, the turnover of crowdsourcing couriers, and unique workload patterns of regular couriers. We design a crowdsourcing-assisted express system called COME to coordinate crowdsourcing and regular couriers for minimizing the overall cost of labor payment and parcel backlog. In COME, we design an Opponent-Aware Reinforcement Learning model to learn the recruitment difficulty in a competitive crowdsourcing market to make an appropriate recruitment plan, and design a four-staged approach to make an appropriate parcel assignment plan, which can address not only the dynamic recruitment difficulty but also the dynamic number of couriers. We have implemented and deployed COME on a real-world crowdsourcing-assisted express system in China involving 1358 delivery stations over 145 cities, and extensively evaluated it with a four-year real-world dataset, demonstrating its great advantage over other alternative solutions and showing high feasibility and generality.

The popularity of online e-commerce has promoted the rapid development of last-mile logistics in recent years. In last-mile services, to ensure delivery efficiency and enhance user experience, the delivery zone is proposed to perform delivery task assignment, which is a fundamental part of last-mile delivery. Each courier is responsible for one delivery zone. Couriers will collect orders belonging to their delivery zones from the delivery station and deliver orders to customers. Existing delivery zone partition practices in last-mile logistics consist of manual experience-based and static optimization-based methods, which perform order amount balancing among different zone but suffer from dissatisfaction and inefficiency because of two limitations: (i) using order amount is not always a good balancing metric considering deliveries' various difficulties (e.g., residence or industrial park, with or without elevators); (ii) less considering couriers' familiarity and preference behaviors. To generate delivery zone partition with equitable workload assignment, in this paper, we propose E-partition, a data-driven delivery zone partition framework to achieve equitable workload assignment in last-mile logistics. We first design a learning-based workload prediction model to estimate service time given a partition plan that consists of unseen courier-zone matching scenarios. Then, a delivery zone partition algorithm is proposed to iterative optimize couriers' core-AOI (i.e., area of interest) generation and AOI assignment process. Extensive offline experimental results show that our model outperforms baselines in working time prediction and workload balancing performances. Real-world deployment results at JD Logistics also verify the effectiveness of equitable-assignment aware delivery zone partition, with a 2.2% increase in service on-time rate compared to state-of-practice partition solutions.

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.

For a nationwide logistics transportation system, it is critical to make the vehicle loading plans (i.e., given many packages, deciding vehicle types and numbers) at each sorting and distribution center. This task is currently completed by dispatchers at each center in many logistics companies and consumes a lot of workloads for dispatchers. Existing works formulate such an issue as a cargo loading problem and solve it by combinatorial optimization methods. However, it cannot work in some real-world nationwide applications due to the lack of accurate cargo volume information and effective model design under complicated impact factors as well as temporal correlation. In this paper, we explore a new opportunity to utilize large-scale route and human behavior data (i.e., dispatchers' decision process on planning vehicles) to generate vehicle loading plans (i.e., plans). Specifically, we collect a five-month nationwide operational dataset from JD Logistics in China and comprehensively analyze human behaviors. Based on the data-driven analytics insights, we design a Vehicle Loading Plan learning model, named VeLP, which consists of a pattern mining module and a deep temporal cross neural network, to learn the human behaviors on regular and irregular routes, respectively. Extensive experiments demonstrate the superiority of VeLP, which achieves performance improvement by 35.8% and 50% for trunk and branch routes compared with baselines, respectively. Besides, we deployed VeLP in JDL and applied it in about 400 routes, reducing the time by approximately 20% in creating plans. It saves significant human workload and improves operational efficiency for the logistics company.

Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services (e.g., Google Maps) by uploading textual addresses to obtain corresponding coordinates. However, this is typically not practical for some location-based service providers due to real-world challenges like commercial competition and high costs (recurring fees). In this paper, we design a new cost-effective Geocoding framework to automatically infer the geographic coordinates from textual addresses for service providers. To achieve this, we take the E-Commerce logistics service as a concrete scenario and design CoMiner, an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data. There are three main components in CoMiner. (1) A POI-level clustering model by modeling customers' shopping patterns at different spatial granularities; (2) A Delivery Mobility Graph (DMG) by modeling couriers' delivery events and geographic coordinates; (3) A behavior-driven address ranking model by mining couriers' uncertain reporting behaviors to further infer coordinates on DMG. We extensively verify the performance of CoMiner with a three-phase evaluation from data-driven experiments to real-world deployment. (i) We conduct extensive experiments on three large-scale datasets where CoMiner achieves an average accuracy of 95.1%, which outperforms the state-of-the-art methods by 20.3%. (ii) We deploy CoMiner in JD Logistics, inferring coordinates for over 30 million addresses with an average accuracy of 93.3%. (iii) We utilize CoMiner for two Geocoding-based applications, i.e., parcel re-routing optimization and abnormal delivery event detection.

An address, a textual description of a physical location, plays an important role in location-based services such as on-demand delivery and e-commerce. However, abnormal addresses (i.e., an address without detailed information representing a spatial location) have led to significant costs. In real-world settings like e-commerce, abnormal address detection is not trivial because it needs to be completed in real-time to support massive online queries. In this study, we design FastAddr, a fast abnormal address detection framework, which detects abnormal addresses among millions of addresses in a short time. By investigating and modeling the hierarchical structure of address data, we first design a novel contrastive address augmentation approach to generate training data via learning the entity transition probability matrix. We further design a lightweight multi-head attention model for learning compact address representation by modeling the address characteristics. We conduct a comprehensive three-phase evaluation. (i) We evaluate FastAddr on a real-world dataset and it yields the average F1 of 85.7% in 0.058 milliseconds, which outperforms the state-of-the-art models by 47.4% with similar detection time. (ii) An offline A/B test shows that FastAddr outperforms the previous deployed model significantly. (iii) We also conduct an online A/B test to compare FastAddr with the deployed model, which shows an improvement of F1 by more than 20%. Moreover, a real-world case study demonstrates both the efficiency and effectiveness of FastAddr.