Title: Algorithmic Optimization of Carbon Footprint in Long-Haul Heavy-Duty E-Truck Transportation
Time: 10:00am
Venue: CB308, HKU
Speaker(s): Minghua Chen
Remark(s):
Abstracct
The US transportation sector accounted for 37% of the country's total CO2 emissions in 2023. While representing only 0.4% of on-road vehicles, long-haul heavy-duty trucks contribute a disproportionate 12% of transportation carbon emissions, making their decarbonization a critical leverage point for climate change mitigation. Electrifying long-haul heavy-duty trucks represents a vital step toward decarbonizing the trucking sector, yet realizing their full potential requires minimizing the carbon footprint of timely deliveries. This involves optimizing electric truck travel between distant locations across the national highway system under strict deadline constraints. The resulting task, encompassing strategic path, speed, and charging planning, is combinatorial in nature and proven NP-hard. Consequently, traditional methods, including our recent approximation algorithms, struggle to optimize at scale. To this end, we present a novel stage-expanded graph formulation that reduces modeling complexity while revealing exploitable problem structure. Our approach naturally decomposes the problem into tractable subproblems, enabling efficient coordination between routing and charging decisions while maintaining manageable graph sizes. Leveraging these structural insights, we design an efficient algorithm with theoretical performance guarantees. Simulations using real-world data across the US highway system demonstrate that our method achieves an additional 25% carbon reduction beyond the 36% reduction from electrification alone, yielding a total 61% emissions decrease. Furthermore, our carbon-optimized strategy, applicable across various truck types, can achieve comparable carbon reductions nine years sooner than relying solely on zero-emission truck adoption, providing a powerful tool in addressing climate change.
About the speaker
Minghua is a Presidential Chair Professor in School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received the Eli Jury award from UC Berkeley in 2007 and The Chinese University of Hong Kong Young Researcher Award in 2013. His recent research interests include online optimization and algorithms, machine learning for optimization with hard constraints and its application in power system operations, intelligent transportation, distributed optimization, and delay-critical networking. He is an ACM Distinguished Scientist and an IEEE Fellow.
