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Wenjun Lyu

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3 papers

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AAAI Conference 2026 Conference Paper

LMGL-WD: LLM-Guided Multi-Task Graph Learning for Category-Level Warehouse Demand Prediction in E-Commerce

Wenjun Lyu
Fangyu Li
Yudong Zhang
Shuai Wang
Yunhuai Liu
Tian He
Desheng Zhang

In warehouse-based e-commerce, accurate category-level warehouse demand prediction is essential to ensure effective inventory management. Existing works mainly explore advanced time series models to capture the temporal dynamics, failing to mine cross-category and cross-warehouse correlations effectively. In this paper, we explore large language models to understand the semantic information and fuse multi-view knowledge to enhance demand prediction. However, it is not trivial due to: i) the inaccurate LLM’s understanding of the category-related and warehouse-related textual input; and ii) the complicated cross-warehouse knowledge utilization. To solve the above challenges, we propose an LLM-guided multi-task graph learning framework, LMGL-WD, for category-level warehouse demand prediction. Specifically, LMGL-WD includes three components: i) an LLM-guided category series encoding module to represent each category through contextual and series embedding; ii) a cross-warehouse category learning module to adaptively mine the informative knowledge from cross warehouses to enhance category representation; and iii) a cross-category multi-task learning module to adaptively capture cross-category correlations to improve demand prediction. Extensive evaluation results with real-world data collected from one of the largest e-commerce platforms in China demonstrate that LMGL-WD achieves superior performance, e.g., reduces MAPE by up to 31.59%, compared to state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Adaptive Multi-Faceted Service Capabilities Co-Prediction for Nationwide Terminal Stations in Logistics

Shuxin Zhong
Kimberly Liu
Wenjun Lyu
Haotian Wang
Guang Wang
Yunhuai Liu
Tian He
Yu Yang

Estimating service capabilities for logistics terminal stations is essential for guiding operations adjustments to enhance customer experience. However, existing studies often focus on isolated metrics like on-time delivery or complaint rates, each reflecting a specific aspect of service capabilities. To provide a more comprehensive evaluation, we design AdaService, an Adaptive multi-faceted Service capabilities co-estimation framework. We begin by constructing Multi-faceted Hypergraph to encode stations using multiple performance metrics. We then introduce a Multi-faceted Hypergraph Convolution Network (MHCN) to capture the heterogeneous service capabilities across stations, providing a comprehensive capabilities representation. Finally, we apply an Adaptive Multi-faceted Estimation module that uses multi-task learning to model dynamic interactions among these metrics, enhancing predictive accuracy. Extensive evaluation with real-world data collected from nationwide stations in a leading logistics company in China demonstrates that AdaService significantly outperforms state-of-the-art methods, improving estimation accuracy for on-time delivery, on-time pick-up, and complaint rates by up to 18.98%, 9.30%, and 39.62%.

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IJCAI Conference 2023 Conference Paper

A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics

Wenjun Lyu
Haotian Wang
Yiwei Song
Yunhuai Liu
Tian He
Desheng Zhang

Order Transfer from the transfer center to delivery stations is an essential and expensive part of the logistics service chain. In practice, one vehicle sends transferred orders to multiple delivery stations in one transfer trip to achieve a better trade-off between the transfer cost and time. A key problem is generating the vehicle’s route for efficient order transfer, i. e. , minimizing the order transfer time. In this paper, we explore fine-grained delivery station features, i. e. , downstream couriers’ remaining working times in last-mile delivery trips and the transferred order distribution to design a Prediction-and-Scheduling framework for efficient Order Transfer called PSOT, including two components: i) a Courier’s Remaining Working Time Prediction component to predict each courier’s working time for conducting heterogeneous tasks, i. e. , order pickups and deliveries, with a context-aware location embedding and an attention-based neural network; ii) a Vehicle Scheduling component to generate the vehicle’s route to served delivery stations with an order-transfer-time-aware heuristic algorithm. The evaluation results with real-world data from one of the largest logistics companies in China show PSOT improves the courier’s remaining working time prediction by up to 35. 6% and reduces the average order transfer time by up to 51. 3% compared to the state-of-the-art methods.

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