Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty
Name
SSRN-id3082518.pdf
Description
Accepted version
Size
1.6 MB
Format
Adobe PDF
Checksum (MD5)
f438ae4e3d760dab24eb333db3f4118b
Author(s)
Lee, Jane
Marla, Lavanya
Jacquillat, Alexandre
Date Issued
July 2020
Journal
Transportation Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Lee, Jane, Marla, Lavanya and Jacquillat, Alexandre. 2020. "Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty." Transportation Science, 54 (4).
Version
Author's final manuscript
Abstract
Air traffic disruptions result in flight delays, cancellations, passenger misconnections, and ultimately high costs to aviation stakeholders. This paper proposes a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. The approach forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruptions. It formulates a dynamic stochastic integer programming framework to minimize network-wide expected disruption recovery costs. Specifically, our Stochastic Reactive and Proactive Disruption Management (SRPDM) model combines a stochastic queuing model of airport congestion, a flight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop a solution procedure based on look-ahead approximation and sample average approximation, which enables the model’s implementation in short computational times. Experimental results show that leveraging even partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1%–2%, as compared with a myopic baseline approach based on realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, flight cancellations, and aircraft swaps.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/trsc.2020.0983
