Deicing decision support tool

Abstract

With today's highly coupled flight schedules, any disturbance to the schedule of a flight could have unforeseen impact on the downstream connecting flights. Thus, it is crucial to be able to minimize the effects ofthe various potential disturbances. Deicing is one such disturbance. During the winter months, deicing procedures associated with snowstorms can cause unexpected delays in the flight schedules. Due to the nonlinear nature of the total time associated with the deicing process (which we call the "total system time," and includes time of waiting and deicing), it is difficult to predict accurately when an aircraft will complete the deicing process. It is difficult, therefore, to predict how the deicing process will influence the subsequent chain of events. In this paper, a queuing model for the deicing process was developed to assist dispatchers to have a more accurate prediction of the completion time for deicing. Moreover, the two major deicing pads at Detroit Metropolitan Wayne County Airport (DTW) were modeled. Through Monte Carlo simulation, estimates of the total system time for the two deicing pads were derived, along with the corresponding 95% confidence intervals. The simulation results were compared to the historical data in the Dynamic Runway Occupancy Measurement System (DROMS), which contains DTW surface surveillance and other aviation related data since October 2002. A decision support tool (DST) was developed, displaying the time estimates. With this deicing DST, airline dispatchers or air traffic controllers can send the next outbound flight to the deicing pad with the least amount of total system time, and, therefore, reduce potential disturbances associated with deicing.

A discrete-event simulation tool for airport deicing activities: Dallas-Fort Worth International Airport

“…12 Simulation of aircraft pilot flight controls has been conducted using the nonlinear system identification technique. 13 Related to ADF-based airport deicing, agent-based simulation 14 has been used to study multiple stakeholders with independent interests, and Monte Carlo simulation 15 has been utilized to estimate the total system time of the deicing process. In our study, the simulation is specifically from the perspective of the airport, and the key element is to allow the airport to control the aircraft-pad location assignments.…”