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Robust Pareto optimal approach to sustainable heavy-duty truck fleet composition
- Sen, Burak, Ercan, Tolga, Tatari, Omer, Zheng, Qipeng Phil
- Resources, conservation, and recycling 2019 v.146 pp. 502-513
- air pollution, economic sectors, electricity generation, freight, greenhouse gas emissions, greenhouse gases, mixing, private organizations, social impact, trucks, United States
- Heavy-duty trucks are the main carrier of the most of freight in the United States today. The U.S. Department of Energy’s projections show that, under the reference case, the truck vehicle-miles-travelled (VMT) on the national highway system will further increase in the near future. This outlook with regard to U.S. Class 8 Heavy-Duty Trucks (HDTs) raises concerns regarding environmental, economic, and social impacts of these vehicles and HDT fleets. However, the transition to sustainable trucking is a challenging task for which multiple sustainability objectives must be considered and addressed, such as minimizing the life-cycle costs (LCCs), life-cycle GHGs (LCGHGs), and life-cycle air pollution externality costs (LCAPECs) of trucks while composing a truck fleet. This study proposes a hybrid life-cycle assessment-based robust Pareto optimal approach to developing a HDT fleet mix, accounting for the sector-specific average payloads of 5 U.S. economic sectors. The results of this study indicate that battery-electric, hybrid, and diesel HDTs make up most of the fleet mixes in the studied sectors in order to optimize their environmental, economic, and social impacts. It is therefore concluded that, given the relevant objectives and constraints, the current techno-economic circumstances in the U.S. and current forms of electricity generation should both be improved in order for HDT fleet mixes to achieve greenhouse gas emission reductions of 30% or greater. The findings of the study will support decision-making processes by public and private organizations and help them to develop environmentally, economically, and socially optimized fleet mixes for their operations.