Optimal Deployment of Emission Reduction Technologies for Large Fleets

In states that have serious air quality problems, such as Texas and California, public fleet managers are under pressure to reduce emissions of their fleets. This paper presents an improved optimization framework for determining the most cost-effective assignment of emission reduction technologies to the vehicles in a large fleet and applies it in a much larger setting than previously done in the literature (in relation to fleet size, geographical area, and numbers of pollutants and technologies). Although the improved framework is applicable to any large setting, this study uses the fleet of the Texas Department of Transportation (DOT) as one example. An explanation of the three components of the framework (i.e., fleet data preprocessing, emissions rate estimation, and the binary integer programming optimization model) is followed by the presentation of two scenarios in which the framework is applied to the whole Texas DOT fleet. The resulting optimal deployment of nine technologies and five pollutants is reported. The improved framework uses weighting schemes for counties and pollutants that allow the optimization model itself to choose the best budget distribution strategy from an arbitrary number of possibilities acceptable to the decision maker. In contrast, previous frameworks could consider only an extremely small number of budget distribution strategies for counties.