부산대학교 도서관

Carbon footprint optimization (CFO) is important for sustainable heavy-duty e-truck transportation. We consider the CFO problem for timely transportation of e-trucks, where the truck travels from an origin to a destination across a national highway network subject to a deadline. The goal is to minimize the carbon footprint by orchestrating path planning, speed planning, and intermediary charging planning. We first show that it is NP-hard even just to find a feasible CFO solution. We then develop a $(1+\epsilon_{F}, 1+\epsilon_{\beta})$ bi-criteria approximation algorithm that achieves a carbon footprint within a ratio of $(1+\epsilon_{F})$ to the minimum with no deadline violation and at most a ratio of $(1+\epsilon_{\beta})$ battery capacity violation (for any positive $\epsilon_{F}$ and $\epsilon_{\beta}$). Its time complexity is polynomial in the size of the highway network, $1/\epsilon_{F}$, and $1/\epsilon_{\beta}$. Such algorithmic results are among the best possible unless $\mathbf{P}=\mathbf{NP}$ Simulation results based on real-world traces show that our scheme reduces up to 11% carbon footprint as compared to baseline alternatives considering only energy consumption but not carbon footprint.