Dataset of an airline-driven flight rescheduling problem

Published: 26 January 2021| Version 1 | DOI: 10.17632/2h9cxkzj8j.1


This data was derived from Luo and Yu (1997), Bard and Mohan (2008), and Brunner (2014). The data is from American Airlines’ daily flight schedule at the Dallas/Fort Worth Airport. There are 71 flights in GDP and the GDP spanned three hours in duration. The planning period of the airline-driven flight rescheduling problem is a half day for one specific season. The data was considered as the base scenario on which a GDP had been issued, and there is no change of the GDP. Including the base scenario, some hypothetical scenarios on subsequent GDPs were assumed and then used to attain the solution. The hypothetical scenarios correspond to new situations in which there are changes in GDP due to weather conditions or external restrictions. The minimum turnaround times of plane and crew were fixed with 30 minutes and 20 minutes, respectively. The maximum allowed time for delays was set to 500 minutes. The misconnection cost of crew members and the cancellation cost of a flight were 50 and 500, respectively. For the incremental delay cost function, each interval, in which different unit costs occurred, was fixed by 60 minutes. There were three intervals. The cost per unit delay time is set to 2, 3, and 4, and associated with each interval, respectively. Correspondingly, the constant value of the incremental delay cost function was set to 0, -60, and -180, and associated with each interval, respectively. The initial flight schedule and the schedule revised by a GDP are presented in "base scenario.csv". The current GDP has been issued at 0. The initial flight schedule defines the arrival and departure times of flights. The connections of crews between flights are also described in the initial flight schedule. In the revised schedule, the arrival times represent the scheduled arrival times of slots. For possible scenarios, ten scenarios were randomly generated. The scheduled arrival times revised by the subsequent GDP in each scenario are presented in scenario##.csv". All the constrained flow rates are 1.81, 1.88, 1.58, 1.64, 1.59, 1.46, 1.49, 1.46, 1.4, and 1.45, respectively. All the next time points are 103, 103, 123, 112, 151, 168, 145, 139, 76, and 103, respectively. In each scenario, the starting times of slots was calculated by adding the inverse of the constrained flow rate to the initial arrival times after the time point, cumulatively. The probability of the base scenario, θ, was set to 0.2, and the probability of the other scenarios was set to 0.08.



Stochastic Optimization, Airport, Airline, Flight