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

Constraint Programming to Improve Hub Utilization in Autonomous Transfer Hub Networks