Multi-Stage Optimal Service Matching Framework for the Logistic Service Combination Problem with Spatio-Temporal Constraints

The fourth-party logistics service platform aims to integrate logistics services from various providers to offer a one-stop logistics solution to customers. However, constructing a logistics service combination scheme that maximizes overall profit while meeting the spatial and temporal constraints of the services is a significant challenge. Traditional heuristic algorithms face issues such as low computational efficiency and insufficient adaptability to dynamic environments and varying order demands when addressing logistics service combination problems with service temporal and spatial constraints. Therefore, we propose the Multi-Stage Optimal Service Matching framework (MOSM). Specifically, the MOSM framework achieves rapid and efficient allocation of logistics order service requirements through three stages: order screening, service provider selection, and service matching, thereby maximizing overall profit. Experiments conducted on nine sets of logistics order datasets demonstrate that the MOSM framework outperforms traditional baseline methods, thereby validating the effectiveness of the MOSM framework in solving logistics service combination problems with temporal and spatial constraints.