Date of Award
Master of Science in Operations Research
Department of Operational Sciences
Andrew J. Geyer, PhD
While modern day weather forecasting is not perfect, there are many benefits given by the multitude and variety of predictive models. In the interest of routing airplanes, this paper uses time series analysis on successive weather forecasts to predict the optimal path and fuel burn of wind-based, fuel-burn networks with stochastic correlated arcs. Networks are populated with either deterministic or ensemble-based weather data, and the two data sources with and without time series analysis are compared. Methods were compared by fuel burn prediction accuracy and ability to predict a future optimal path. Of the four options, the ensemble-based methods were on average the least accurate. Using time series analysis on ensemble data gave a nominal change in correct future path prediction and an increase in fuel burn pre- diction accuracy. The deterministic method gave the most accurate results but the worst correct future path prediction rate. Time series analysis on deterministic data had a marginal decrease in accuracy but the highest correct future path prediction rate.
DTIC Accession Number
Sands, Brendon T., "Time Series Analysis of Stochastic Networks with Correlated Random Arcs" (2019). Theses and Dissertations. 2315.