This dissertation investigates how to improve airline fuel efficiency by changing fuel loading behavior of airline dispatchers and reducing unnecessary fuel loading. First, we estimate the potential benefit of fuel loading reduction for six major U.S.-based airlines. We find that the annual monetary savings per airline of avoiding carrying unused fuel in 2012 ranges from $42 million to $605 million, with a total across all six airlines of $1.16 billion. This suggests that there may be significant benefit from reducing unnecessary fuel loading in the U.S. airline industry.

Second, to capture that benefit, we first study the behavior of dispatchers – who make fuel loading decisions – based on comprehensive historical fuel burn data provided by a major U.S.-based airline. Risk-neutral and risk-averse newsvendor models are applied to better understand how dispatchers trade off safety concerns due to possibly insufficient fuel loading and extra fuel burn costs due to excess discretionary fuel loading. We find that dispatchers place extremely high priority on safety relative to excess fuel cost in making discretionary fuel loading decisions. Furthermore, combining our results with a dispatcher survey, we also find that dispatchers who are detail oriented and conservationists are likely to load less discretionary fuel. Our results imply that airlines may want to select for these characteristics during dispatcher interview and seek to cultivate such behavior in dispatcher recurrent training.

Finally, besides behavioral modeling of dispatchers, we propose two novel discretionary fuel estimation approaches that can assist dispatchers with better discretionary fuel loading decisions. Based on the analysis on our study airline, our approaches are found to substantially reduce unnecessary discretionary fuel loading while maintaining the same safety level compared to the current fuel loading practice. The idea is that by providing dispatchers with more accurate information and better recommendations derived from flight records and related contextual data, unnecessary fuel loading and corresponding cost-to-carry could both be reduced. The first approach involves applying ensemble machine learning techniques to improve fuel burn prediction and construct prediction intervals (PI) to capture the uncertainty of model predictions. The upper bound of a PI can be used for discretionary fuel loading. The potential benefit of this approach is estimated to be $61.5 million in fuel savings and 428 million kg of CO2 reduction per year for our study airline. The second approach in estimating discretionary fuel originates from the idea of statistical contingency fuel (SCF). Due to limitations in the current SCF estimation method, dispatchers have low confidence in applying SCF values and generally load more discretionary fuel than recommended. Therefore, improved SCF estimation offers another practical approach for reducing discretionary fuel loading. The estimated annual benefit of using this approach is $19 million in fuel savings and 132 million kg CO2 emissions reductions for our study airline. A similar analysis could be easily generalized to other airlines or the industry as a whole when such detailed airline fuel data becomes available.