Group Stable Matching Problem in Freight Pooling Service of Vehicle–Cargo Matching Platform - Linlin Kong; Min Huang (Systems, 2025)
With the continuous advancement of the Internet and information technologies, the capacity for development and integration of vehicle and cargo resources has been significantly enhanced, driving the rapid emergence of vehicle–cargo matching platforms. Serving as critical intermediaries between vehicle owners and cargo owners, vehicle–cargo matching platforms effectively address key challenges in traditional logistics, such as low matching efficiency and information asymmetry. As a result, they significantly improve the intelligence and precision of logistics resource allocation. However, at the current stage, vehicle–cargo matching platforms rarely promote freight pooling services, leading to resource underutilization. Due to the freight pooling matching problem involving the combination and allocation of multiple vehicle owners and cargo owners, which is highly complex, few scholars have conducted research on such issues. The lack of coordinated optimization in matching models may result in inefficiencies, and the limited consideration of individual user preferences can lead to low user satisfaction. Therefore, this paper focuses on the freight pooling matching problem in vehicle–cargo matching platforms. To improve matching efficiency and fully consider user preferences, the theory of stable matching is introduced into the freight pooling matching problem. It defines the concepts of combination preferences and group stability based on combination preferences, establishes a group stable matching model for the freight pooling business of vehicle–cargo matching platforms, and verifies the stability of the model through theoretical proof. Since this model is a mixed-integer linear programming model with relatively few decision variables but a large number of constraints, this paper introduces the cutting-plane algorithm. Based on the characteristics of the problem, the algorithm is improved by removing ineffective constraints and only using key constraints, significantly reducing computational complexity, optimizing the solving process, and greatly improving the model’s solution efficiency. This approach aligns well with the characteristics of the vehicle–cargo freight-pooling matching model. The research results indicate that the group stable matching model significantly improves platform revenue, vehicle owners’ profits, and cargo owners’ satisfaction across various supply and demand scenarios. Additionally, the cutting-plane algorithm reduces computation time by 97% and decreases the number of constraints during the solving process by 99%. The stable matching theory and solution algorithm proposed in this paper can provide users with precise matching schemes, significantly improving matching efficiency, user satisfaction, platform revenue and platform competitiveness. It demonstrates high innovation and practical application value.