Autonomous Truck Scheduling and Platooning Considering Cargo Consolidation

Thanks to advancements in automated driving technology, autonomous trucks (ATs) can form platoons with minimal inter-vehicle distances on highways, significantly reducing air drag and fuel consumption for fleets. Given the dispersed distribution and small quantities of cargo, fleet operators should manage ATs to enable cargo consolidation during platooning. In this way, fleet operators can enhance operational efficiency and reduce fuel consumption. This study addresses the AT scheduling and platooning problem considering cargo consolidation. The problem is the scheduling of ATs to transport cargo while consolidating cargo and forming platoons between two terminals, all while minimizing operational costs. A mixed-integer linear programming (MILP) model is formulated for the proposed problem. In addition, we conduct extensive numerical experiments to evaluate the proposed model. The results show that Gurobi can solve instances with different sizes to optimality or near-optimality. Impact analysis is also conducted to explore the influences of several factors, such as maximal platoon size and the load capacity of AT, on the system performance and to provide managerial insights.