  The Autonomous Transfer Hub Network (ATHN) is one of the most promising ways
to adapt self-driving trucks for the freight industry. These networks use
autonomous trucks for the middle mile, while human drivers perform the first
and last miles. This paper extends previous work on optimizing ATHN operations
by including transfer hub capacities, which are crucial for labor planning and
policy design. It presents a Constraint Programming (CP) model that shifts an
initial schedule produced by a Mixed Integer Program to minimize the hub
capacities. The scalability of the CP model is demonstrated on a case study at
the scale of the United States, based on data provided by Ryder System, Inc.
The CP model efficiently finds optimal solutions and lowers the necessary total
hub capacity by 42%, saving $15.2M in annual labor costs. The results also show
that the reduced capacity is close to a theoretical (optimistic) lower bound.
