Abstract: Hydrogen fuel cell trucks (HFCTs) are a promising alternative to diesel trucks (DTs) for decarbonizing long-haul freight transportation while maintaining comparable operational performance. Despite continued advancements in hydrogen technologies, research on the strategic deployment of hydrogen refueling stations (HRSs) along the U.S. continental interstate highway network remains limited, particularly at a national scale.

The resulting infrastructure planning problem is computationally challenging due to the large size of the refueling amount set. To address this challenge, we develop a hybrid solution framework that integrates Benders decomposition with row generation (BD--RG) to significantly improve computational efficiency. The proposed approach is capable of solving practical-scale instances with up to 20 demand scenarios within moderate times.

In addition, we investigate several algorithmic enhancements, including the derivation of Pareto-optimal cuts, the incorporation of valid inequalities, greedy warm start, and the use of parallel computing. Computational experiments demonstrate the effectiveness and scalability of the proposed algorithms.