Minimizing Carbon Footprint for Timely E-Truck Transportation: Hardness and Approximation Algorithm

• Main Authors: Su, Junyan, Lin, Qiulin, Chen, Minghua, Zeng, Haibo
• Format: Journal Article
• Language: English
• Published: 18.08.2023
• Subjects: Computer Science - Data Structures and Algorithms
• DOI: 10.48550/arxiv.2308.09866

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 P=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.