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

• Published in: arXiv.org
• Main Authors: Su, Junyan, Lin, Qiulin, Chen, Minghua, Zeng, Haibo
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 19.08.2023
• Subjects: Algorithms; Alternative energy sources; Approximation; Carbon; Carbon footprint; Energy consumption; Footprint analysis; Mathematical analysis; Optimization; Polynomials; Truck transportation
• ISSN: 2331-8422

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.