Green and Cooperative Task-and-Route Optimization for Container Trucks with Heterogeneous Carriers Based on Task Sharing

To address the issues of capacity resource waste and increased carbon emissions caused by the asymmetry between import and export container transportation tasks in port collection and dispatching, a green and cooperative task-and-route optimization method for container trucks with heterogeneous carriers based on task sharing is proposed from the perspective of system optimization. Based on the concept of a sharing economy, a sharing and cooperation mechanism with dual elasticity in capacity and information is designed, which integrates the container trucks’ resources and dissymmetric transportation tasks of heterogeneous carriers to expand the revenue potential for all participants. Based on task sharing and matching, a green and cooperative task-and-route optimization model for container trucks with heterogeneous carriers based on task sharing is formulated in order to optimize container trucks’ resources and transportation tasks comprehensively and reduce the system’s carbon emissions. A column generation algorithm embedded with a ring-increasing strategy is designed to solve the problem to improve computational efficiency. Through algorithm testing and a case analysis, the effectiveness of the model and algorithm is validated. The optimization results show that the overall carbon emissions are reduced by more than 28%, the number of used trucks decreases by 28%, and the profits of participants are increased by 24–65% compared with independent operations. Finally, several management insights are obtained regarding the number of shared trucks, the external market demand, task demand variability, the mixed fleet composition, subsidies, and bonus adjustments.