Optimizing Freight Operations for Autonomous Transfer Hub Networks

TL;DR

This paper introduces a constraint programming model to optimize autonomous transfer hub networks in freight, demonstrating potential cost savings and operational efficiency through a detailed real-world case study.

Contribution

It presents the first detailed quantitative optimization model for ATHNs, showing their feasibility and benefits in freight operations.

Findings

CP can solve large-scale ATHN scheduling problems efficiently.

ATHNs can significantly reduce transportation costs.

Autonomous transfer hubs improve freight efficiency.

Abstract

Autonomous trucks are expected to fundamentally transform the freight transportation industry, and the technology is advancing rapidly. According to some of the major players, the Autonomous Transfer Hub Network (ATHN) business model is the most likely future for the industry. ATHNs make use of transfer hubs to hand off trailers between human-driven trucks and autonomous trucks. Autonomous trucks then carry out the transportation between the hubs, while conventional trucks serve the first and last mile. This paper presents a Constraint Programming (CP) model to schedule the ATHN operations to perform a given set of orders. The model is used to, for the first time, provide a detailed quantitative study of the benefits of ATHNs by considering a real case study where actual operations are modeled and optimized with high fidelity. It is found that solving this large-scale optimization problem with CP is computationally feasible, and that ATHNs may bring significant cost savings.