【公开学术报告】Data-Driven Aircraft Assignment to Minimize Delay Propagation

Data-Driven Aircraft Assignment to Minimize Delay Propagation

Guest Speaker:     Dr. Wei LIU (UNC)

Date & Time:       9:30-11:00 (Beijing Time), Thursday 14th, Dec. 2023

Zoom Meeting: 95206149831（Password: 486914）

Click the Link:     https://zoom.us/j/95206149831

ABSTRACT

Flight delays have a significant impact on an airline's operating cost including increased expenses for crew, fuel, and maintenance. Propagated delays due to late arriving aircraft contribute to 40% of all fight delays as reported by the Bureau of Transportation Statistics. The aircraft assignment problem is to assign tail numbers on scheduled arriving fights at an airport to scheduled departing fights at the same airport with the objective of minimizing propagated delays. In this paper, we propose a new data-driven approach for the aircraft assignment problem by formulating it as a balanced assignment problem between incoming and outgoing fights own by the same aircraft type at a single airport.

We consider both deterministic and stochastic versions of the aircraft assignment problem. In the deterministic case, we prove the optimality of the First-in-First-out (FIFO) assignment policy under two different performance measures, justifying the use of the FIFO policy as a benchmark. In the stochastic case, we show that the FIFO assignment policy is no longer optimal and propose the rFIFO and stochastic assignment formulations based on the mean and empirical distribution of arrival delay, respectively.

We propose a data-driven approach to estimate the assignment costs by using empirical observations of arrival delays from prior years' fight records to compute the empirical propagated delay distribution. We propose a data-driven clustering method to account for factors such as originating airport, time of day, and aircraft type that affect the arrival delay distribution. These empirical cluster based aircraft assignment costs serve as an input to our stochastic assignment model. These assignment costs are then used to derive the optimal aircraft assignment for the rFIFO and stochastic assignment policies for an out of sample data set from 2018 for Delta airlines at Atlanta airport.

We show that both the rFIFO and stochastic assignment policies derived from the data-driven approach perform better than the benchmark FIFO assignment. We also show that for all Delta airline fights at Atlanta airport from July to September in 2018, the stochastic assignment policy yields a roughly 18% improvement in total actual propagated delay over the actual airline assignment while reducing the fraction of delayed fights due to propagated delays from 6.89% to 3.14%, thus potentially saving approximately 6.5 million dollars in fight delay related annual operating costs. We conclude that incorporating the stochastic nature of arrival delays in solving the aircraft assignment problem, and combining it with a data-driven approach can potentially help an airline significantly reduce its operating costs due to fight delays, improve passenger convenience and experience and help the environment by reducing emissions.

Key words: data-driven research; aircraft assignment; delay propagation; stochastic optimization