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Abstract
AbstractHydrogen trucks (HTs) offer promising potential for decarbonizing the transportation sector. Based on current technologies, they have significant advantages over electric trucks (ETs) in terms of range, refueling time, and performance in cold conditions. However, HTs are costly, and there are insufficient hydrogen refueling stations (HRSs). Gradually integrating HTs into the existing diesel truck (DT) fleet is a practical approach for many freight logistics companies. In this article, we formulate a mathematical model to route a mixed fleet of HTs and DTs, and we propose an algorithm called the curve descent search (CDS) to generate the Pareto set based on cost and carbon emissions. We find that CDS can generate better Pareto sets compared to existing algorithms in the literature. We use CDS to comprehensively explore the cost–carbon trade‐off in using a mixed fleet. This question differentiates our study from previous research and is motivated by discussions with one of the largest third‐party logistics companies in North America. Detailed experiments reveal important managerial insights, such as: (1) Achieving a significant reduction in carbon emissions (e.g., a 30% reduction compared to the current diesel fleet) does not need a very dense refueling infrastructure; (2) The cost–carbon trade‐off for mixed fleets is relatively insensitive to variations in customer density and demand, suggesting that HTs can be applicable across a wide range of scenarios (including different sectors or regions); and (3) Although ETs are cheaper to use compared to HTs, their shorter range limits their competitiveness in terms of decarbonization efficiency and customer service.
Published Version
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