Managing and owning a diverse fleet of vehicles encompass several essential factors including vehicle selection (make, model, fuel type, etc.), maintenance, refueling, capital cost, and sustainability. However, many organizations lack a systematic approach to evaluate and transition to a cost-efficient and low-emission fleet composition under varying operational conditions. As one of the major contributors to emissions, the transportation sector aims to reduce tailpipe emissions through various strategies. A comprehensive plan for procuring, salvaging, storing, and strategically managing the fleet can result in a sustainable and cost-efficient fleet. Because of their lower maintenance, fuel, emissions, and operations costs, alternative fuel vehicles (AFVs) could pave the way toward a sustainable fleet. This study aims to develop an optimization framework that identifies the most cost- and emission-efficient fleet composition strategy under real-world operational and environmental conditions. In this study, the emissions of the vehicles, estimated by the EPA’s MOVES simulation tools, and the real-world dataset from the Michigan State University (MSU) fleet are employed to calibrate emission regression models embedded within the proposed optimization framework. The proposed integer linear mathematical optimization model suggests the optimal fleet composition and operations plan while the total fleet cost (including the monetary and societal cost of emissions) is minimized. Various operational and environmental conditions, such as the vehicles’ seasonal VMT, speed, idle time, age, and temperature are considered in the framework. The results of deploying the proposed optimization framework express a rapid transition toward AFVs, besides a reduction of 38% to 74% in emissions, 40% to 78% in fossil fuels consumption, and up to 15% in total fleet cost depending on the emission and fuel types and the fleet operating scenario.
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