Abstract Truck-cargo matching is one of the core tasks of online freight platforms, where the primary objective is to optimally assign each cargo task to the most suitable truck. The existing matching strategies seek to maximize total transported weight of cargoes by increasing the number of truck-cargo pairings. However, these strategies fail to ensure global matching pair maximization across all regions due to the heterogeneous spatial distribution of truck supply and cargo transporting demand. This limitation necessitates the incorporation of regional supply–demand gap prediction into the matching process. Two critical challenges emerge in achieving optimal matching: (1) the prediction accuracy of supply–demand gaps is influenced by multiple complex factors, and (2) the multi-objective optimization conflicts within current matching decisions may adversely impact subsequent matching performance. To address these challenges, we propose a hierarchical reinforcement learning framework for multi-objective truck-cargo matching, comprising two key components: a supply–demand gap prediction module and a multi-objective optimization matching module.For accurate supply–demand gap prediction, we develop a hypergraph attention network model incorporating an adaptive confidence interval optimization mechanism to capture complex relationships among various predictive factors. Furthermore, to mitigate negative effects of multi-objective conflicts on long-term matching performance, we design a hierarchical deep Q network model that dynamically adjusts objective weights based on predicted long-term benefits. Extensive experiments conducted on two real-world logistics datasets demonstrate that our proposed method achieves a 10.7% higher competitive ratio compared to state-of-the-art approaches, validating the effectiveness of our supply–demand balance guided matching strategy in practical operational scenarios.

The rapid development of steel logistics industry still has not effectively address such issues as truck overload and order overdue as well as cargo overstock. One of the reasons lie in limited number of trucks for transporting large scale cargos. More importantly, traditional methods attend to distribute cargos to trucks with the aim of maximizing the loading of each truck. But they ignore the priority level of orders and the expiration date of cargos stored in the warehouses, which have critical influences on profits of steel logistics industry. Hence, it necessitates an appropriate cargo distribution mechanism under the precondition of limited transportation capacity resources, to guarantee the maximization of delivery proportion for high-priority cargos. Recently, tremendous logistics data has been produced and are being in constant increment hourly in steel logistics platform. However, there is no existing solution to transform such data into actionable scheme to improve cargo distributing effectiveness. This paper puts forward a system implementation of smart cargo loading plan decision framework (SCLPD for short) for steel logistics industry. Through analysis on numerous real data cargo loading plan and inventory of warehouse, some important rules related to cargo distribution process are extracted. Additionally, consider that different amounts of trucks arriving in different time periods, based on adaptive time window model, a two- layer searching mechanism consisting of a genetic algorithm and A* algorithm is designed to ensure global optimization of cargo loading plan for the trucks in all time periods. In our demonstration, we illustrate the procedure of matching for cargos and trucks in various time windows, and showcase the comparison experimental results between the traditional method and SCLPD by the measurement of delivery proportion for high- priority cargos. The effectiveness and practicality of SCLPD enables efficient cargo loading plan generation, to meet the real- world requirements from steel logistics platform.