Incorporating Predictability Into Cost Optimization for Ground Delay Programs

This work introduces the goal of predictability into ground delay program (GDP) cost optimization under capacity uncertainty for a single airport case. This is accomplished by modifying traditional GDP delay cost functions so that they incorporate predictability. Extra premiums are assigned to unplanned delays, and planned but unincurred delays, due to their unpredictability. Two stochastic GDP models are developed based on deterministic queueing theory and continuous approximation to estimate the delay components in the cost functions: a static no-revision model and a dynamic model considering one GDP revision. The decision on the time when the planned airport arrival capacity moves from the low level to the normal level, T, is made to minimize the expected GDP cost. The case study shows that, by varying the unpredictability premiums within plausible ranges, the optimal value T* can be anywhere from the 33rd to the 87th percentile of the distribution of the actual capacity recovery time. Of the two unpredictability premiums, the one for unplanned delay has a stronger impact on T* than the one for planned but unincurred delay. In general, reducing GDP scope increases T* except when planned delay is almost equally costly whether or not it is incurred. Depending on the values of unpredictability premiums, cost savings of as much as 13% can be realized from properly valuing unpredictability in GDP decision making.